Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Attending to Future Tokens for Bidirectional Sequence Generation (D19-1)
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| Challenge: | Neural sequence generation is typically performed token-by-token and left-to-right. |
| Approach: | They propose to use placeholder tokens to make the sequence generation process bidirectional by taking past and future tokens into consideration when generating the actual output token. |
| Outcome: | The proposed approach outperforms baselines on two conversational tasks by a large margin. |
Attention is not not Explanation (D19-1)
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| Challenge: | Attention mechanisms play a central role in NLP systems, especially within recurrent neural network (RNN) models. |
| Approach: | They propose to use a simple uniform-weights baseline, a variance calibration and a diagnostic framework to determine when/whether attention can be used as explanation in RNN models. |
| Outcome: | The proposed tests show that even reliable adversarial distributions don't perform well on the simple diagnostic, indicating that prior work does not disprove the usefulness of attention mechanisms for explainability. |
Practical Obstacles to Deploying Active Learning (D19-1)
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| Challenge: | Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. |
| Approach: | They propose to use active learning to optimize predictive performance . they find that current approaches do not generalize reliably across models and tasks . |
| Outcome: | The proposed approach outperforms training on i.i.d. datasets on supervised learning tasks. |
Transfer Learning Between Related Tasks Using Expected Label Proportions (D19-1)
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| Challenge: | Existing methods of data supervision are limited by labeled training data. |
| Approach: | They propose a method where models are trained based on expected label proportions. |
| Outcome: | The proposed method improves on a sentence-level sentiment predictor and is cumulative with LM-based pretraining. |
Knowledge Enhanced Contextual Word Representations (D19-1)
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Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A. Smith
| Challenge: | Existing methods to embed knowledge bases into large pre-training models do not contain any explicit grounding to real world entities and are difficult to recover factual knowledge. |
| Approach: | They propose a method to embed multiple knowledge bases (KBs) into large pretrained models with a Knowledge Attention and Recontextualization mechanism. |
| Outcome: | The proposed model improves perplexity, ability to recall facts and word sense disambiguation. |
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (D19-1)
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| Challenge: | Existing word embeddings were static, requiring all senses of a polysemous word to share the same representation. |
| Approach: | They found that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. |
| Outcome: | The results show that the representations of all words are not isotropic in any layer of the contextualizing model. |
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
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| Challenge: | Word embeddings are increasingly used for automatic detection of semantic change, but a robust evaluation and systematic comparison of the choices involved has been lacking. |
| Approach: | They propose a new evaluation framework for semantic change detection using whole time series and a Twitter dataset spanning 5.5 years. |
| Outcome: | The proposed framework shows that using whole time series is preferable over continuously trained embeddings for long time periods and that the reference point matters. |
Correlations between Word Vector Sets (D19-1)
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| Challenge: | Similarity measures based on word embeddings are easily competing with deep learning and expert-engineered systems on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a new approach to measure word embeddings using pooling operations and correlation coefficients instead of pooling . they also propose centered kernel alignment as a natural generalisation of squared cosine similarity for sets of word vectors. |
| Outcome: | The proposed approach outperforms most recent methods while being much faster and trivial to implement. |
Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing (NLP). |
| Approach: | They propose a game-theoretic model that embeds ambiguous words as players of a non cooperative game and their senses as strategies that the players can select in order to play the games. |
| Outcome: | The proposed model performs well on standard benchmarks and different tests on standard datasets. |
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog (D19-1)
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| Challenge: | Existing methods to learn dialog policy require elaborate design and user goals. |
| Approach: | They propose an algorithm that estimates the reward signal and infers the user goal in dialog sessions. |
| Outcome: | The proposed algorithm achieves higher task success than state-of-the-art models on a multi-domain task-oriented dialog dataset. |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
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| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
MoEL: Mixture of Empathetic Listeners (D19-1)
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| Challenge: | Neural network approaches for conversation models have shown to be successful in generating fluent and relevant responses. |
| Approach: | They propose a novel end-to-end approach for modeling empathy in dialogue systems by using Mixture of Empathetic Listeners (MoEL). |
| Outcome: | The proposed model outperforms multitask training baseline in terms of empathy, relevance, and fluency. |
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
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| Challenge: | Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other. |
| Approach: | They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history . |
| Outcome: | The proposed framework outperforms baseline models and produces entity-consistent responses. |
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation (D19-1)
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| Challenge: | Current approaches to visual dialog learning involve an end-to-end framework that maps the multi-modal context to a deep vector and in order to decode a natural dialog response. |
| Approach: | They propose a framework that trains a RL policy for image guessing and a seq2seq model to improve dialog quality. |
| Outcome: | The proposed framework achieves state-of-the-art performance on a guessWhich task . it can be applied to a wide range of tasks including assisting blind people . |
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (D19-1)
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
| Outcome: | The proposed method outperforms the current state-of-the-art on a number of benchmark emotion classification datasets while minimizing context propagation issues. |
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (D19-1)
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| Challenge: | Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions . |
| Approach: | They propose a Knowledge-Enriched Transformer where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged. |
| Outcome: | The proposed model outperforms state-of-the-art models on most of the tested datasets in F1 score. |
Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)
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| Challenge: | Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words . |
| Approach: | They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events. |
| Outcome: | The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions. |
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
| Approach: | They propose an ‘extractive’ approach to identify review segments which justify users’ intentions and use it to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets. |
| Outcome: | The proposed model can generate convincing and diverse justifications from massive review corpora and distantly label massive review data. |
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)
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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
| Challenge: | Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification . |
| Approach: | They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity. |
| Outcome: | The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)
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| Challenge: | Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive. |
| Approach: | They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data. |
| Outcome: | Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods. |
Improving Relation Extraction with Knowledge-attention (D19-1)
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| Challenge: | Existing attention mechanisms are data-driven, but most are data driven. |
| Approach: | They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
| Outcome: | The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset. |
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion (D19-1)
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| Challenge: | Recent studies have focused on the large proportion of infrequent relations which have been ignored by previous studies. |
| Approach: | They propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. |
| Outcome: | The proposed framework outperforms existing methods when dealing with infrequent relations and uncommon entities. |
Low-Resource Name Tagging Learned with Weakly Labeled Data (D19-1)
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| Challenge: | Existing methods for name tagging in low-resource languages or domains require extensive human efforts for training annotations. |
| Approach: | They propose a neural model for name tagging based on weakly labeled (WL) data. |
| Outcome: | The proposed model outperforms existing models in five low-resource languages and fine-grained food domains and shows that it is more efficient and efficient than existing models. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
Learning to Bootstrap for Entity Set Expansion (D19-1)
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| Challenge: | Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision. |
| Approach: | They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals. |
| Outcome: | The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. |
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text (D19-1)
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| Challenge: | Existing methods to extract factual tuples from scientific text do not consider conditions. |
| Approach: | They propose a new sequence labeling framework to jointly extract fact and condition tuples from scientific sentences. |
| Outcome: | The proposed framework improves F1 score relative to existing methods by 4.2% and 6.2% on bioNLP2013. |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)
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| Challenge: | Existing event extraction methods are limited to extract event arguments within the sentence scope. |
| Approach: | They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively. |
| Outcome: | The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively. |
Event Detection with Trigger-Aware Lattice Neural Network (D19-1)
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| Challenge: | Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese. |
| Approach: | They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two benchmark datasets and outperformed previous state- of-the art methods significantly. |
A Boundary-aware Neural Model for Nested Named Entity Recognition (D19-1)
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| Challenge: | Existing methods for named entity recognition ignore nested entities . a boundary-aware neural model can locate entities precisely by detecting boundaries . |
| Approach: | They propose a boundary-aware neural model for nested named entity recognition which leverages entity boundaries to predict entity categorical labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on GENIA dataset . it captures dependencies of entity boundaries and categorical labels, which helps to improve identifying entities. |
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning (D19-1)
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| Challenge: | Existing works didn’t consider the extraction order of relational facts in a sentence. |
| Approach: | They propose to take the extraction order into consideration by applying reinforcement learning into a sequence-to-sequence model. |
| Outcome: | The proposed model could generate relational facts freely. |
CaRe: Open Knowledge Graph Embeddings (D19-1)
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| Challenge: | Existing methods for generating Open Knowledge Graphs have been criticized for not achieving canonicalization of OpenKGs. |
| Approach: | They propose to use Open Information Extraction methods to extract triples from text . they propose to learn embeddings of NPs and RPs present in the graph . |
| Outcome: | The proposed methods improve OpenKG embeddings and bootstrap OpenKGs from text corpus. |
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction (D19-1)
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| Challenge: | Distantly Supervised Relation Extraction (DSRE) suffers from mislabelled data . human annotation on large datasets is costly and often impossible . |
| Approach: | They propose a model that employs a collaborative curriculum learning framework to reduce mislabelled data. |
| Outcome: | The proposed model outperforms baselines including state-of-the-art in terms of P@N and PR curve metrics on a widely-used public dataset. |
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
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| Challenge: | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities. |
| Approach: | They propose a bilingual word embedding mapping approach for cross-lingual RE model transfer . they use a small bilingual dictionary with only 1K word pairs to embed word pairs . |
| Outcome: | The proposed approach achieves very good performance on target and target languages . it uses bilingual word embedding mapping to transfer a source-language model . |
Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction (D19-1)
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| Challenge: | Existing methods to construct noisy labeled data for relation extraction (RE) are expensive and lacks the labeling capability. |
| Approach: | They propose a 2-hop DS strategy to enhance distantly supervised relation extraction (RE) by combining sentences that mention entities that are linked to each other. |
| Outcome: | The proposed method outperforms baselines on a benchmark dataset by a substantial margin. |
EntEval: A Holistic Evaluation Benchmark for Entity Representations (D19-1)
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| Challenge: | EntEval is a test suite of tasks that require nontrivial understanding of entities. |
| Approach: | They propose to encode the mention context or the Wikipedia hyperlink annotations to learn better entity representations. |
| Outcome: | The proposed model improves strong baselines on multiple EntEval tasks. |
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)
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| Challenge: | Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification. |
| Approach: | They propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. |
| Outcome: | The proposed method improves both event extraction and temporal relation extraction over state-of-the-art systems. |
Hierarchical Text Classification with Reinforced Label Assignment (D19-1)
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| Challenge: | Existing hierarchical text classification methods make local decisions regarding labels or ignore hierarchy information during inference. |
| Approach: | They propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. |
| Outcome: | The proposed method outperforms state-of-the-art methods on five datasets and four base models and achieves an average improvement of 33.4% over flat classifiers. |
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification (D19-1)
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| Challenge: | Various neural networks are designed for text classification on the basis of word embedding, but polysemy is a fundamental feature of the natural language, which brings challenges to text classification. |
| Approach: | They propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. |
| Outcome: | The proposed model extracts more discriminative semantic features and yields significant performance gain compared to baseline methods. |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
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| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis . |
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification (D19-1)
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| Challenge: | Existing methods for text classification are based on large-scale labeled data, but few data are available. |
| Approach: | They propose a hierarchical attention prototypical networks for few-shot text classification . they use attention mechanism to highlight or weaken the importance of features, words, and instances . |
| Outcome: | The proposed model can capture more important features, words, and instances . it can also increase support set augmentability and accelerate convergence speed in training stage . |
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification (D19-1)
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| Challenge: | Feature importance is commonly used to explain machine predictions . however, the consistency of feature importance via different methods remains understudied . |
| Approach: | They compare feature importance from built-in mechanisms and post-hoc methods that approximate model behavior to find similarities between models. |
| Outcome: | The proposed methods show that features from traditional models are more similar with each other than with deep learning models. |
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification (D19-1)
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| Challenge: | Existing methods for text classification learn long dependency by deeply stacking or hybrid modeling. |
| Approach: | They propose a global-based local feature extraction architecture with global information incorporated into the local feature extractor. |
| Outcome: | The proposed architecture outperforms the previous best models on eight benchmark datasets. |
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
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| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (D19-1)
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| Challenge: | Existing approaches to reviewer-paper matching are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. |
| Approach: | They propose to combine the common topic model and abstract topic vectors to model the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. |
| Outcome: | The proposed model improves on the existing model on two datasets. |
Linking artificial and human neural representations of language (D19-1)
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| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
| Approach: | They compare sentence encoding models with fMRI-based fMR predictions of the sentence . they use a pre-trained BERT architecture as a baseline and fine-tune it on a variety of natural language understanding (NLU) tasks. |
| Outcome: | The proposed model does not yield significant improvements in brain decoding performance on the natural language understanding (NLU) tasks. |
Neural Text Summarization: A Critical Evaluation (D19-1)
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| Challenge: | Current approaches to text summarization use advanced attention and copying mechanisms, multi-task and multi-reward training techniques. |
| Approach: | They evaluate datasets, evaluation metrics, and models for text summarization . they highlight three primary shortcomings: 1) datasets leave task underconstrained; 2) models overfit layout biases . |
| Outcome: | The current evaluation protocol is weakly correlated with human judgment and does not account for factual correctness. |
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures (D19-1)
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| Challenge: | Traditionally, data-to-text applications have been designed using a modular pipeline architecture, in which the non-linguistic input data is converted into natural language through several intermediate transformations. |
| Approach: | They propose to use Gated-Recurrent Units and Transformer to implement neural pipelines for data-to-text generation. |
| Outcome: | The proposed models generalize better to unseen inputs and have better performance than the existing pipeline architectures. |
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (D19-1)
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| Challenge: | Existing evaluation metrics are not capable of evaluating text quality. |
| Approach: | They propose a metric that compares system output against reference texts based on semantics rather than surface forms. |
| Outcome: | The proposed metric shows a high correlation with human judgment of text quality on a number of text generation tasks. |
Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
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| Challenge: | Recent neural network models conflate content selection and surface realization into a black-box architecture, resulting in content to be described in text cannot be explicitly controlled. |
| Approach: | They propose to decouple content selection from the decoder to allow finer-grained control over the generation. |
| Outcome: | The proposed model can be trained end-to-end without human annotations and achieves promising results in data-totext and headline generation tasks. |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling (D19-1)
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| Challenge: | Unlike annotation projection techniques, our model does not need parallel data during inference time. |
| Approach: | They propose a cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with semantic role annotations in a resource-poor target language. |
| Outcome: | The proposed model can be applied in monolingual, multilingual and cross-lingual settings and produces dependency-based and span-based annotations. |
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)
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| Challenge: | Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL. |
| Approach: | They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively. |
| Outcome: | The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset. |
VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling (D19-1)
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| Challenge: | VerbAtlas is a lexical-semantic resource that combines WordNet synsets into semantically-coherent frames. |
| Approach: | They propose a lexical-semantic resource that brings together WordNet synsets into semantically-coherent frames. |
| Outcome: | The proposed resource brings together all WordNet synsets into semantically-coherent frames. |
Parameter-free Sentence Embedding via Orthogonal Basis (D19-1)
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| Challenge: | Existing methods to build sentence embeddings are parameterized and require training to optimize their parameters. |
| Approach: | They propose a non-parameterized method to combine pre-trained word embeddings into sentence representations using an orthogonal basis of the word vector subspace and its surrounding context. |
| Outcome: | The proposed method shows superior performance on 11 downstream NLP tasks and is competitive to other methods relying on large amounts of labelled data or prolonged training time. |
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations (D19-1)
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| Challenge: | Prior work on pretrained sentence embeddings and benchmarks focused on the capabilities of stand-alone sentences. |
| Approach: | They propose a test suite of tasks to evaluate whether sentence representations include broader context information. |
| Outcome: | The proposed training objectives help to encode different aspects of information in document structures. |
Extracting Possessions from Social Media: Images Complement Language (D19-1)
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| Challenge: | Existing studies show that authors of tweets possess objects they tweet about. |
| Approach: | They propose a dataset and experiments to determine whether tweet authors possess objects they tweet about. |
| Outcome: | The proposed strategy incorporates visual information into any neural network beyond weights from pretrained networks. |
Learning to Speak and Act in a Fantasy Text Adventure Game (D19-1)
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Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |
Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning (D19-1)
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| Challenge: | HANNA is an interactive photo-realistic simulator that can help agents with navigation tasks . human assistants are rich external knowledge sources but may not be available all the time to provide guidance . |
| Approach: | They develop a photo-realistic mobile agent simulator that asks for help from humans . they use natural language and visual instructions to direct agents towards the goals . |
| Outcome: | The proposed approach can accomplish tasks more effectively than competing models . it can also predict its own chances of making future progress . |
Incorporating Visual Semantics into Sentence Representations within a Grounded Space (D19-1)
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| Challenge: | Language grounding is an active field aiming at enriching textual representations with visual information. |
| Approach: | They propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space. |
| Outcome: | The proposed model outperforms the previous state-of-the-art on classification and semantic relatedness tasks. |
Neural Naturalist: Generating Fine-Grained Image Comparisons (D19-1)
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| Challenge: | a dataset of 41k sentences describes fine-grained differences between photographs of birds . human observers are adept at making fine-grain comparisons, but sometimes require aid in distinguishing visually similar classes. |
| Approach: | They propose a model that generates comparative language from a dataset of 41k sentences describing fine-grained differences between photographs of birds. |
| Outcome: | The proposed model can explain differences in visual embedding space using natural language . it evaluates the results with humans who must use the descriptions to distinguish real images . |
Fine-Grained Evaluation for Entity Linking (D19-1)
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| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
| Approach: | They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems. |
| Outcome: | The proposed task offers a bridge between unstructured text and structured KBs, where EL has applications for semantic search, document classification, relation extraction, and more. |
Supervising Unsupervised Open Information Extraction Models (D19-1)
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| Challenge: | Existing supervised methods that use labeled training data are expensive and difficult to adapt to new domains. |
| Approach: | They propose a supervised open information extraction framework that leverages unsupervised Open IE systems and labeled data to improve system performance. |
| Outcome: | The proposed method outperforms existing supervised and unsupervised models by a significant margin. |
Neural Cross-Lingual Event Detection with Minimal Parallel Resources (D19-1)
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| Challenge: | Existing methods for event detection (ED) rely on high-performance machine translation systems or manually aligned documents to achieve a decent performance. |
| Approach: | They propose a method that uses context-dependent translation to construct a lexical mapping between different languages and a shared syntactic order event detector for multilingual co-training. |
| Outcome: | The proposed method performs cross-lingual transfer and tackles the extremely annotation-poor scenario. |
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population (D19-1)
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| Challenge: | KnowledgeNet provides text exhaustively annotated with facts . high-quality KBs still rely almost exclusively on human-curated structured or semi-structured data. |
| Approach: | They propose five baseline approaches to populating a knowledge base with facts . the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% . |
| Outcome: | The best approach achieves an F1 score of 0.50, outperforming a traditional approach by 79%, indicating the dataset is challenging. |
Effective Use of Transformer Networks for Entity Tracking (D19-1)
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| Challenge: | Existing pre-trained language models for entity-related tasks are not able to handle the nuances of procedural text. |
| Approach: | They propose to use pre-trained transformer networks to track entities in procedural text by restructuring input to focus on a particular entity. |
| Outcome: | The proposed models outperform baseline models on ingredient detection in recipes and QA over scientific processes on two different tasks. |
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (D19-1)
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| Challenge: | Existing approaches to build initial unsupervised machine translation models with cross-lingual n-gram embeddings are inexplicit and limited. |
| Approach: | They propose a cross-lingual pre-training method that incorporates cross-linguistic training signals into pre-trained models by randomly choosing source n-grams in the input text stream. |
| Outcome: | The proposed method significantly improves the performance of unsupervised machine translation. |
Latent Part-of-Speech Sequences for Neural Machine Translation (D19-1)
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| Challenge: | Existing methods for learning target side syntactic structure are greedy and only allow them to explore a limited portion of the latent space. |
| Approach: | They propose a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics while allowing for effective inference over the latent space. |
| Outcome: | The proposed model captures the co-dependence between syntax and semantics while allowing for efficient inference over the latent space. |
Improving Back-Translation with Uncertainty-based Confidence Estimation (D19-1)
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| Challenge: | Despite the success of low-resource neural machine translation, there is a data scarcity problem in many languages . large-scale, high-quality, and widecoverage bilingual corpora do not exist for most language pairs . |
| Approach: | They propose to quantify confidence of NMT models based on model uncertainty . they propose to use uncertainty-based confidence measures to improve back-translation . |
| Outcome: | The proposed model outperforms conventional statistical machine translation (SMT) on Chinese-English and English-German translation tasks. |
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (D19-1)
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| Challenge: | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use dot product-based functions to define dot products over embeddings to better capture semantics of 1-N, N-1 and N-N relations. |
| Outcome: | The proposed framework outperforms existing methods on multilingual datasets. |
Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages (D19-1)
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| Challenge: | Existing methods for mapping monolingual word embeddings into another are based on anchor points and unsupervised methods are more adversarial. |
| Approach: | They propose a noise-tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. |
| Outcome: | The proposed method outperforms the state-of-the-art in lower resourced settings with an average of 3.7% improvement of precision @10 across 14 mostly low resourced languages. |
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (D19-1)
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| Challenge: | Pretrained contextual representation models have pushed forward the state-of-the-art on many NLP tasks. |
| Approach: | They propose to use a model that is pretrained on 104 languages for cross-lingual transfer. |
| Outcome: | The proposed model performs well on 5 NLP tasks covering 39 languages from various language families. |
Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework. |
| Approach: | They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer. |
| Outcome: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework. |
Multi-agent Learning for Neural Machine Translation (D19-1)
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| Challenge: | Experimental results show that training with more than one agent improves translation quality and improves accuracy. |
| Approach: | They propose to introduce diverse agents in an in- teractive updating process to train NMT models with an additional agent. |
| Outcome: | The proposed approach improves on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German translation tasks and shows competitive performance on all tasks. |
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (D19-1)
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| Challenge: | Using parallel corpora, we train a single, direct NMT model for non-English language pairs. |
| Approach: | They propose three ways to increase the relation among source, pivot, and target languages in pre-training . they use additional adapter component to smoothly connect pre-trained encoder and decoder . |
| Outcome: | The proposed methods outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. |
Context-Aware Monolingual Repair for Neural Machine Translation (D19-1)
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| Challenge: | et al., 2018) show that human raters prefer corrected translations over the baseline ones. |
| Approach: | They propose a monolingual model to correct inconsistencies between sentences . they use monolingual document-level data to train the model . |
| Outcome: | The proposed model improves translations of contextual phenomena in English-Russian translation task. |
Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)
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| Challenge: | Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information. |
| Approach: | They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms. |
| Outcome: | The proposed approach improves on English-to-German and NIST Chinese-to English translation tasks. |
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention (D19-1)
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| Challenge: | Existing methods to improve NLP convergence and computational overhead are limited by stacking more layers. |
| Approach: | They propose a depth-scaled initialization method which reduces parameter variance at initialization and reduces output variance of residual connections to ease gradient back-propagation. |
| Outcome: | The proposed method outperforms the base model on translation tasks with five translation directions while matching the decoding speed of the baseline model. |
A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
| Approach: | They propose a discriminative word alignment model which integrates into a Transformer-based machine translation model. |
| Outcome: | The proposed model performs better on Chinese and Arabic alignments than standard models. |
One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)
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| Challenge: | Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language. |
| Approach: | They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner . |
| Outcome: | The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models. |
Dynamic Past and Future for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation models can benefit from modeling translated and untranslated source contents as recurrent states, but this less interpretable recurrence hinders their power to model dynamic updating of and contents during decoding. |
| Approach: | They propose to model the dynamic updating of and contents during decoding by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. |
| Outcome: | The proposed method achieves significant improvements over both Rnmt and Transformer by producing more adequate translations. |
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
| Approach: | They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs. |
| Outcome: | The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs. |
Towards Understanding Neural Machine Translation with Word Importance (D19-1)
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| Challenge: | Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory. |
| Approach: | They propose to attribute NMT output to every input word using a gradient-based method to measure word importance. |
| Outcome: | The proposed method is superior on identifying input words with higher influence on translation performance. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
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| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction (D19-1)
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| Challenge: | Human translators have to translate rare inflections due to Zipfian distribution of words in a language. |
| Approach: | They introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the best performing models on the task of translation of less frequent morphology. |
| Outcome: | The proposed models perform better on infrequent morphological inflections and add a simple constraint at training time. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
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| Challenge: | Recent advances in morphological inflection generation have limited resources . antonisa and colleagues present a battery of improvements to improve performance under low-resource conditions . |
| Approach: | They propose a two-step attention architecture for the inflection decoder that uses two-segments attention and a multi-single-syllabic attention architecture. |
| Outcome: | The proposed model outperforms the state-of-the-art in low-resource languages by 15 percentage points . the proposed model also shows that it can be used to model monolingual data hallucinations . |
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank (D19-1)
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| Challenge: | Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. |
| Approach: | They propose to map dependency arcs from source treebank to target translation according to word alignments. |
| Outcome: | Experiments on university dependency treebanks show that translated treebank translations are more effective than translated treebans. |
Hierarchical Pointer Net Parsing (D19-1)
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| Challenge: | Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps. |
| Approach: | They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks. |
| Outcome: | The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets. |
Semi-Supervised Semantic Role Labeling with Cross-View Training (D19-1)
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| Challenge: | Recent approaches rely on expensive annotations and are unavailable in low resource scenarios (e.g., rare languages or domains). |
| Approach: | They propose an end-to-end SRL model which leverages unlabeled data and propose to reduce the annotation effort involved via semi-supervised learning. |
| Outcome: | The proposed model outperforms the state-of-the-art in English and consistently improves performance in other languages, including Chinese, German, and Spanish. |
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations (D19-1)
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| Challenge: | Existing approaches to cross-lingual sequence labeling require bilingual resources and require linguistic knowledge. |
| Approach: | They propose a multilingual language model with deep semantic Alignment to generate language-independent representations for cross-lingual sequence labeling. |
| Outcome: | The proposed model achieves state-of-the-art NER and POS performance across European languages and on distant language pairs such as English and Chinese. |
A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)
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| Challenge: | Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure. |
| Approach: | They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features. |
| Outcome: | The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics. |
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)
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| Challenge: | Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance. |
| Approach: | They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner. |
| Outcome: | The proposed network outperforms existing models on two benchmarks and a self-collected corpus. |
Tree Transformer: Integrating Tree Structures into Self-Attention (D19-1)
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| Challenge: | Existing work on hierarchical structure in neural networks has not captured human intuitions about hierarchic structures. |
| Approach: | They propose to add an extra constraint to attention heads of the bidirectional Transformer encoder to encourage attention heads to follow tree structures. |
| Outcome: | The proposed model improves language modeling and learning more explainable attention scores. |
Semantic Role Labeling with Iterative Structure Refinement (D19-1)
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| Challenge: | Modern state-of-the-art methods for semantic role labeling model only local interactions between individual labels . |
| Approach: | They propose to model local interactions between argument labeling decisions using a refinement network instead of arbitrary interactions between roles and words. |
| Outcome: | The proposed model outperforms baseline models on all 7 languages and achieves state-of-the-art results on 5 languages, including English. |
Entity Projection via Machine Translation for Cross-Lingual NER (D19-1)
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| Challenge: | a subset of languages have large annotated corpora for named entity recognition. |
| Approach: | They propose a system that leverages machine translation systems twice to improve named entity recognition. |
| Outcome: | The proposed system outperforms existing methods on Armenian languages by 4.1 points . it achieves state-of-the-art F_1 scores for Armenian, outperforming monolingual model trained on Armenia. |
A Bayesian Approach for Sequence Tagging with Crowds (D19-1)
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| Challenge: | Existing methods for sequence tagging are data hungry and annotators are unreliable . current methods do not account for common types of span annotation error . |
| Approach: | They propose a Bayesian method for aggregating sequence tags that models sequential dependencies between annotations and ground-truth labels. |
| Outcome: | The proposed method outperforms existing methods on crowdsourced data and reduces crowdsourcing costs through active learning. |
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing (D19-1)
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| Challenge: | Existing work on cross-lingual dependency parsing focuses on capturing commonalities between source and target languages and overlooking the potential to leverage the linguistic properties of the target languages to facilitate the transfer. |
| Approach: | They propose to use Lagrangian relaxation and posterior regularization techniques to conduct inference with corpus-statistics constraints to capture commonalities between source and target languages. |
| Outcome: | The proposed algorithms improve on 15 and 17 out of 19 target languages. |
Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
| Outcome: | The proposed parser improves on one-shot parsing by 68.8% compared to baselines . it adapts the logical forms of seen utterances to fit the unseen utterant . |
Similarity Based Auxiliary Classifier for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) tasks are a fundamental challenge for name recognition tasks that aim to reduce the boundary error when entities become longer. |
| Approach: | They propose a similarity based auxiliary classifier which can distinguish entity words from non-entity words by using vectors to indicate tags. |
| Outcome: | Empirical results show that the proposed classifier can perform better than baseline approaches. |
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal (D19-1)
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| Challenge: | a variable beam size inference method is proposed for generative parsing for RNNG . the proposed method is not sensitive to lexical biases faced by standard beam search . |
| Approach: | They propose a method of variable beam size inference for Recurrent Neural Network Grammar by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. |
| Outcome: | The proposed method is based on a generative parsing framework that can be used to model brain activity during online sentence comprehension. |
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets (D19-1)
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| Challenge: | Having only a few workers generate the majority of dataset examples raises concerns about data diversity . |
| Approach: | They perform a series of experiments to investigate annotator biases in recent NLU datasets . they find that models are able to recognize the most productive annotators . |
| Outcome: | The results show that models can recognize the most productive annotators and do not generalize well to examples from annotator that did not contribute to the training set. |
Robust Text Classifier on Test-Time Budgets (D19-1)
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| Challenge: | Recent advances in deep neural networks (DNNs) achieve high accuracy on many text classification tasks. |
| Approach: | They propose a generic framework for learning a robust text classification model . they use a data aggregation method to train the classifier on a large corpus of text . |
| Outcome: | The proposed framework achieves consistent speedup with little degradation in accuracy on four benchmark text classification tasks. |
Commonsense Knowledge Mining from Pretrained Models (D19-1)
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| Challenge: | Several approaches have been proposed for training models for commonsense knowledge base completion (CKBC) due to the sparsity of training data. |
| Approach: | They propose a method for generating commonsense knowledge using a large, pre-trained bidirectional language model by transforming relational triples into masked sentences. |
| Outcome: | The proposed method outperforms models trained on held-out test sets on a held-up set, suggesting that it generalizes better than current supervised methods. |
RNN Architecture Learning with Sparse Regularization (D19-1)
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| Challenge: | State-of-the-art NLP models require hundreds of millions and even billions of parameters to perform, which can lead to memory usage and increased runtime. |
| Approach: | They propose a structure learning method that uses group lasso to learn sparse, parameter-efficient NLP models by pruning more than 90% of the weights of rational RNNs. |
| Outcome: | The proposed method learns sparse, parameter-efficient models without sacrificing performance relative to parameter-rich baselines. |
Analytical Methods for Interpretable Ultradense Word Embeddings (D19-1)
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| Challenge: | Word embeddings lack interpretability, but rotation of word spaces can help . e.g., lexicon induction, gender bias can be removed by removing interpretable dimensions . |
| Approach: | They propose three methods for making word embeddings interpretable by rotation . they use Densifier, linear SVMs and DensRay to compute word spaces in closed form . |
| Outcome: | The proposed method can be computed in closed form and is more robust than Densifier. |
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (D19-1)
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| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
| Outcome: | The proposed model outperforms strong baselines on the GLUE benchmark and can be adapted to new tasks efficiently and effectively. |
Retrofitting Contextualized Word Embeddings with Paraphrases (D19-1)
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| Challenge: | Contextualized word embeddings can be useful for downstream applications, but they can be over-sensitive to contexts. |
| Approach: | They propose a method to retrofit contextualized word embeddings with paraphrases to minimize the variance of word representations on paraphrased contexts. |
| Outcome: | The proposed method improves on sentence classification and inference tasks. |
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)
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| Challenge: | Semantic similarity modeling is central to many NLP problems such as question answering. |
| Approach: | They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness. |
| Outcome: | Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines. |
Neural Linguistic Steganography (D19-1)
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| Challenge: | linguistic steganography encrypts a secret message into a cover signal . language is a pragmatic cover signal due to its benign occurrence and independence from any one medium. |
| Approach: | They propose a technique that encrypts a secret message into a cover signal . language is a particularly pragmatic cover signal due to its benign occurrence . |
| Outcome: | The proposed technique generates realistic looking cover sentences as evaluated by humans while preserving security by matching the cover message distribution with the language model distribution. |
The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization (D19-1)
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| Challenge: | Existing evaluation methods for summarization systems measure semantic overlap between a system summary and a human reference on word-string level. |
| Approach: | They propose to use distributed representations to evaluate system summary and human reference on word-string level. |
| Outcome: | The proposed representations outperform ROUGE on recent corpora but are less good on test data used in previous studies. |
Attention Optimization for Abstractive Document Summarization (D19-1)
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| Challenge: | Abstractive summarization models require attention to reproduce the most salient information. |
| Approach: | They propose to use local and global variances to augment the vanilla attention model to reproduce the most salient information and avoid repetitions. |
| Outcome: | The proposed attention refinement unit can reproduce the most salient information and avoid repetitions on CNN/Daily Mail dataset. |
Rewarding Coreference Resolvers for Being Consistent with World Knowledge (D19-1)
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Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
| Challenge: | Unresolved coreference is a bottleneck for relation extraction systems . a state-of-the-art system may be able to infer the relation using distributional information about the phrase the Sunshine State, but is likely to have limited evidence for the decision that it is coreferential with Florida rather than with Skynyrd. |
| Approach: | They propose to forward coreference input to relation extraction system and reward them for producing triples that are found in knowledge bases. |
| Outcome: | The proposed approach improves over the state-of-the-art by forwarding their input to a relation extraction system and rewarding resolvers for producing triples that are found in knowledge bases. |
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction (D19-1)
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| Challenge: | incorporating pseudo data in the training of grammatical error correction models has been a key factor in improving performance of such models. |
| Approach: | They investigate the choice of how pseudo data should be generated or used in a grammatical error correction model and show that the results are state-of-the-art. |
| Outcome: | The proposed method achieves state-of-the-art on the CoNLL-2014 test set and the official test set of the BEA-2019 shared task without making any modifications to the model architecture. |
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability (D19-1)
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| Challenge: | Existing models implicitly assume that documents in different languages are highly comparable, a false assumption. |
| Approach: | They propose a multilingual topic model that learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar. |
| Outcome: | The proposed model outperforms existing models in low-resource language tasks and outperformed LDA and previous models in classification tasks using documents’ topic posteriors as features. |
Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study (D19-1)
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| Challenge: | a large number of indicators are difficult to measure, such as unemployment rate . a novel approach to measure socio-economic indicators with news events is proposed . |
| Approach: | They propose an event-centric indicator measure to extract news events from streaming news . they show strong correlations between ECIM values and representative indicators . |
| Outcome: | The proposed method is effective and correlated with several indicators . it is based on events reported in streaming news . |
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
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| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue (D19-1)
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| Challenge: | Existing methods to generate natural language for task-oriented dialogues lack naturalness and variation in language. |
| Approach: | They propose a multi-task learning framework for natural language generation that explicitly targets for naturalness in generated responses via an unconditioned language model. |
| Outcome: | The proposed framework outperforms existing models across multiple datasets in the study of natural language generation. |
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation (D19-1)
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| Challenge: | Existing work assumes the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively. |
| Approach: | They propose to use the Dirichlet distribution with flexible structures to characterize latent variables in place of the Gaussian priors. |
| Outcome: | The proposed model outperforms existing models on the dialogue generation task. |
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling (D19-1)
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| Challenge: | Existing systems rely on modular, domain-focused frameworks for analyzing complex problems. |
| Approach: | They propose semi-supervised learning methods that can reduce the amount of required intermediate labelling by leveraging un-annotated data instead of transcribed utterances. |
| Outcome: | The proposed model reduces the amount of turn-level annotations by 30% while maintaining equivalent system performance. |
A Progressive Model to Enable Continual Learning for Semantic Slot Filling (D19-1)
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| Challenge: | Existing approaches to slot filling training on large scale data are inefficient and require multiple trainings. |
| Approach: | They propose a slot filling model that transfers previously learned knowledge to a small size expanded component and enables it to be fast trained to learn from new data. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets by 4.24% and 3.03% on the same dataset. |
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots (D19-1)
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| Challenge: | Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. |
| Approach: | They propose a context-aware self-attentive NLU model that uses multiple signals over a variable context window, such as previous intents, slots, dialog acts and utterances, in addition to the current user uttered. |
| Outcome: | The proposed model outperforms a baseline model on two conversational datasets yielding a gain of up to 7% on the IC task. |
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)
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| Challenge: | Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data. |
| Approach: | They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies. |
| Outcome: | The proposed learning method improves the performance of matching models on two benchmarks with three matching models. |
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (D19-1)
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| Challenge: | a lack of research on multilingual or cross-lingual task-oriented dialog systems has limited results . we propose a zero-shot adaptation of task-orientated dialog systems to low-resource languages . task-focused systems are often trained with monolingual datasets that are expensive to build or acquire . |
| Approach: | They propose a zero-shot adaptation of multilingual task-oriented dialog systems to low-resource languages using latent variables and a set of very few parallel word pairs. |
| Outcome: | The proposed model performs better in natural language understanding task compared to state-of-the-art model . the proposed model uses very few parallel word pairs to refine cross-lingual representations . |
Modeling Multi-Action Policy for Task-Oriented Dialogues (D19-1)
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| Challenge: | Existing approaches to learn dialogue management only predict one action per turn, limiting expressive power of the conversational agent and introducing unnecessary turns of interactions. |
| Approach: | They propose a model based on a recurrent cell called gated Continue-Act-Slots that overcomes the limitations of existing models and proposes a novel policy model that predicts multiple acts for each turn. |
| Outcome: | The proposed model outperforms existing models on the task of predicting multiple acts for each turn. |
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (D19-1)
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Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
| Challenge: | Task-oriented dialog systems need to know when a query falls outside their range of supported intents. |
| Approach: | They propose a dataset that includes queries that are out-of-scope and 150 intent classes over 10 domains. |
| Outcome: | The proposed dataset includes queries that are out-of-scope, i.e., queries that do not fall into any of the system’s supported intents. |
Automatically Learning Data Augmentation Policies for Dialogue Tasks (D19-1)
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| Challenge: | Recent advances in automatic data augmentation have focused on computer vision tasks where it is easy to apply imperceptible perturbations without changing an image’s semantic meaning. |
| Approach: | They adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. |
| Outcome: | The proposed algorithm reduces data-level model bias by using a controller trained on the target task. |
uniblock: Scoring and Filtering Corpus with Unicode Block Information (D19-1)
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| Challenge: | Existing methods to remove sentences consisting of illegal characters are tedious and repetitive. |
| Approach: | They propose a statistical method to identify illegal characters in natural language processing . they use a fixed-size feature vector to generate a Gaussian mixture model for each sentence . |
| Outcome: | The proposed method can score sentences and filter corpus on clean corpus and improve performance. |
Multilingual word translation using auxiliary languages (D19-1)
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| Challenge: | Existing multilingual word translation methods focus on learning mappings from each language to a shared space. |
| Approach: | They propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another. |
| Outcome: | Experiments on a standard multilingual word translation benchmark show that the proposed translation procedure outperforms state-of-the-art translation methods. |
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)
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| Challenge: | Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. |
| Approach: | They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition. |
| Outcome: | The proposed model outperforms both individual models and a standard hybrid model on a machine translation task. |
Vecalign: Improved Sentence Alignment in Linear Time and Space (D19-1)
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| Challenge: | Sentence-aligned bitext is used to train nearly all machine translation systems. |
| Approach: | They propose a bilingual sentence alignment method which is linear in time and space with respect to the number of sentences being aligned. |
| Outcome: | The proposed method outperforms the existing method by 5 F1 points on a German–French test set and improves downstream MT quality by 1.7 and 1.6 BLEU in Sinhala-English and Nepali-English, respectively. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (D19-1)
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| Challenge: | Contextual word embeddings have demonstrated state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to use adversarial learning to improve upon multilingual BERT's zero-resource cross-lingual performance by aligning embeddings of English documents and their translations. |
| Outcome: | The multilingual version of BERT performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. |
Recurrent Positional Embedding for Neural Machine Translation (D19-1)
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| Challenge: | Existing translation systems that use positional embeddings only encode static order dependencies based on discrete numerical information, which may hinder the improvement of translation capacity. |
| Approach: | They propose a recurrent positional embedding approach based on word vectors that are learned by a neural network and integrated into existing multi-head self-attention models. |
| Outcome: | The proposed approach improves translation performance over the state-of-the-art Transformer baseline in English-to-German and NIST Chinese-to English translation tasks. |
Machine Translation for Machines: the Sentiment Classification Use Case (D19-1)
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| Challenge: | Traditionally, machine translation (MT) pursues a "human-oriented" objective: generating fluent output for a downstream task. |
| Approach: | They propose a neural machine translation approach that uses weak feedback to generate translations that are best suited for a downstream task. |
| Outcome: | The proposed approach outperforms general-purpose models and reinforcement learning methods on German and Italian tweets. |
Investigating the Effectiveness of BPE: The Power of Shorter Sequences (D19-1)
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| Challenge: | Byte-Pair Encoding (BPE) is an unsupervised sub-word tokenization technique, but its reasons for its effectiveness are not well understood. |
| Approach: | They link BPE to the broader family of dictionary-based compression algorithms and compare it with other members of this family. |
| Outcome: | The proposed method is compared with dictionary-based compression algorithms and improves on a fixed vocabulary size budget. |
HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation (D19-1)
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| Challenge: | Existing methods to incorporate bilingual lexicons into statistical machine translation are unclear how to do so in the neural framework. |
| Approach: | They present a dataset to test methods for bilingual lexicon integration into neural machine translation using human generated alignments of words and phrases in three language pairs. |
| Outcome: | The proposed method improves on baselines and improves training to address overfitting. |
Handling Syntactic Divergence in Low-resource Machine Translation (D19-1)
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| Challenge: | Existing approaches to neural machine translation (NMT) are dependent on limited parallel data, and can be difficult to use for many language pairs. |
| Approach: | They propose a method where target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. |
| Outcome: | The proposed method improves on simulated low-resource Japanese-to-English and real low-demand Uyghur-to English scenarios. |
Speculative Beam Search for Simultaneous Translation (D19-1)
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| Challenge: | Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial. |
| Approach: | They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model. |
| Outcome: | The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search. |
Self-Attention with Structural Position Representations (D19-1)
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| Challenge: | Experimental results show that SANs can't encode positions of input words . SAN's are currently lacking in encoding positions of words based on position-unaware "bagof-words" theory . |
| Approach: | They propose to augment SANs with structural position representations to capture latent structure of input sentence. |
| Outcome: | The proposed approach consistently outperforms the sequential representations on translation tasks. |
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation (D19-1)
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| Challenge: | Using multi-parallel corpora for transfer learning is a useful technique for low-resource NMT. |
| Approach: | They compare multi-parallel corpora for transfer learning in a low-resource setting . their results show that multi-paralleled corpors are extremely useful . |
| Outcome: | The proposed model can give 3–9 BLEU score gains over a one-to-one model. |
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings (D19-1)
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| Challenge: | Recent studies have focused on domain adaptation for neural machine translation systems where in-domain data is scarce or nonexistent. |
| Approach: | They propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. |
| Outcome: | The proposed model performs better in multiple experimental settings and with back translation. |
A Regularization-based Framework for Bilingual Grammar Induction (D19-1)
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| Challenge: | Existing multilingual grammar induction methods require external resources such as parallel corpora, word alignments or linguistic phylogenetic trees. |
| Approach: | They propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. |
| Outcome: | The proposed method outperforms baselines on transfer grammar induction and bilingual grammar inducing on multiple languages. |
Encoders Help You Disambiguate Word Senses in Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation models can perform word sense disambiguation (WSD) however, it is unclear which component dominates the process of disambiguating words. |
| Approach: | They evaluate hidden states and investigate distributions of self-attention in NMT encoders and decoders to disambiguate word senses. |
| Outcome: | The proposed model outperforms encoder hidden states on large datasets . the model outpersforms decoders on large data sets . |
Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model (D19-1)
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| Challenge: | Korean morphological analysis is a sequence of morpheme processing and POS tagging. |
| Approach: | They propose a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. |
| Outcome: | The proposed model achieves state-of-the-art performance without any explicit regularization. |
Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)
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| Challenge: | Diacritic restoration is a computational task that requires a computer to understand written texts. |
| Approach: | They propose to use Temporal Convolutional Neural Networks (TCN) to restore missing diacritics for each character in written text. |
| Outcome: | The proposed model improves on TCN in Arabic, Yoruba, and Vietnamese. |
Improving Generative Visual Dialog by Answering Diverse Questions (D19-1)
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| Challenge: | Prior work on training generative Visual Dialog models with reinforcement learning has shown that this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. |
| Approach: | They propose a Q-Bot-A-Bot image-guessing game that allows Q-BOT to ask diverse questions, thus reducing repetitions and enabling A-BOTT to explore a larger state space during RL. |
| Outcome: | The proposed approach improves Q-Bot-A-Bot image-guessing performance but degrades after a few rounds of interaction and does not lead to a better Visual Dialog model. |
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding (D19-1)
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| Challenge: | Existing approaches to improve cross-lingual transfer learning on spoken language are pre-train on all available supervised data from another language. |
| Approach: | They propose a language model based source-language data selection method for cross-lingual transfer learning in spoken language understanding. |
| Outcome: | The proposed method reduces training time and improves model performance on spoken language understanding. |
Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations (D19-1)
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| Challenge: | Recent studies have advanced learning VSE under the monolingual setup. |
| Approach: | They propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations by leveraging visual object detection. |
| Outcome: | The proposed model performs well in German-Image and English-Image matching tasks and in the Semantic Textual Similarity task with English descriptions of visual content. |
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering (D19-1)
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| Challenge: | Object detection is used in vision and language tasks but is expensive to learn . popular models rely on annotating ground-truths for bounding boxes and semantic labels . empirically, object detection leads to effective transfer learning and improved captioning and visual question answering models . |
| Approach: | They examine the effect of decoupling box proposal and featurization on down-stream tasks . they propose a family of "two-stage" object detectors that propose category-agnostic bounding boxes . |
| Outcome: | The proposed method improves image captioning and visual question answering models by leveraging large amounts of labeled annotations. |
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)
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| Challenge: | Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors. |
| Approach: | They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. |
| Outcome: | The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics. |
WSLLN:Weakly Supervised Natural Language Localization Networks (D19-1)
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| Challenge: | Existing methods to learn correspondence between visual segments and texts require temporal coordinates for training, which leads to high costs of annotation. |
| Approach: | They propose weakly supervised language localization networks to detect events in untrimmed videos . they train with only video-sentence pairs without accessing to temporal locations of events . |
| Outcome: | Experiments on ActivityNet Captions and DiDeMo show that WSLLN performs state-of-the-art. |
Grounding learning of modifier dynamics: An application to color naming (D19-1)
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| Challenge: | Existing models for grounding are unable to understand modified color expressions, such as “light blue”. |
| Approach: | They propose a model that learns more complex transformations in RGB space and a hard ensemble model that selects a color space depending on the modifier-color pair. |
| Outcome: | The proposed model performs better in the HSV color space than the state-of-the-art model. |
Robust Navigation with Language Pretraining and Stochastic Sampling (D19-1)
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Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi
| Challenge: | Existing methods to learn visual representations and action decoding schemes are limited to previously unseen instructions and environments. |
| Approach: | They propose a stochastic sampling scheme to reduce the gap between the expert actions in training and sampled actions in test to correct its own mistakes. |
| Outcome: | The proposed methods achieve 6% absolute gain over the previous best results on the Room-to-Room benchmark. |
Towards Making a Dependency Parser See (D19-1)
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| Challenge: | Eye trackers and gaze features collected from them have been recently applied to natural language processing (NLP) tasks such as part-of-speech tagging. |
| Approach: | They propose to leverage eye-tracking data in an RNN dependency parser when no aggregated or token-level gaze features are used at inference time. |
| Outcome: | The proposed model can be used to improve performance on non-gazed treebanks. |
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders (D19-1)
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| Challenge: | Existing models that use ground-truth part-of-speech tags are not always available and have significant weaknesses. |
| Approach: | They propose to use deep inside-outside recursive autoencoders to cluster the learned phrase vectors to induce span labels. |
| Outcome: | The proposed model outperforms ELMo and BERT on two versions of the Wall Street Journal dataset and improves over a previous state-of-the-art system that requires additional human annotations by 5 absolute F1 points (19% relative error reduction). |
Dependency Parsing for Spoken Dialog Systems (D19-1)
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| Challenge: | Dependency parsing of conversational input can help to understand dialogs . currently available annotation schemes do not adapt well to spoken human-machine dialogs. |
| Approach: | They propose an annotation scheme that extends Universal Dependencies guidelines to spoken dialogs. |
| Outcome: | The proposed scheme disambiguates relationships between entities extracted from dialogs . it is better than existing models on public datasets and fine-tuned on ConvBank data . |
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog (D19-1)
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| Challenge: | Existing semantic parsers score intents and slots as labels of nesting nodes, but decode a valid tree globally. |
| Approach: | They propose a span-based semantic parser for parsing compositional utterances into Task Oriented Parse (TOP) the parsers score labels of the tree nodes covering each token span independently, but decode a valid tree globally. |
| Outcome: | The proposed parser outperforms previous methods on the TOP dataset in accuracy and training speed. |
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation (D19-1)
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| Challenge: | Context modeling is essential to generate coherent and consistent translation for document-level Neural Machine Translations. |
| Approach: | They propose a query-guided capsule network to cluster context information into different perspectives from which the target translation may concern. |
| Outcome: | The proposed model outperforms baseline models on multiple datasets of different domains. |
Simple, Scalable Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Recent advances in deep learning have led to significantly improved quality on Neural Machine Translation (NMT) however, performance on out-of-domain data or low resource languages remains poor. |
| Approach: | They propose a simple yet efficient approach for adapting pre-trained models to multiple tasks simultaneously. |
| Outcome: | The proposed approach is on par with full fine-tuning on domain adaptation and massively multilingual NMT on a massively multilingual dataset. |
Controlling Text Complexity in Neural Machine Translation (D19-1)
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| Challenge: | Prior work on text complexity has focused on simplifying input text in one language, primarily English. |
| Approach: | They propose a method to align news articles written for different levels of target language proficiency. |
| Outcome: | The proposed model outperforms pipeline approaches that translate and simplify text independently. |
Investigating Multilingual NMT Representations at Scale (D19-1)
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| Challenge: | Multilingual Neural Machine Translation models have shown success in transfer learning settings, but their mode of transfer remains elusive. |
| Approach: | They propose to use a representation similarity framework to compare multilingual representations using a SVCCA representation similar to the previous work. |
| Outcome: | The proposed model can be used to compare representations across languages and layers. |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Cross-Lingual Machine Reading Comprehension (D19-1)
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (D19-1)
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| Challenge: | Existing models for reading comprehension and question answering do not support discrete reasoning abilities. |
| Approach: | They propose a reading comprehension model that uses a multi-type answer predictor and a multiple-span extraction method to produce one or multiple text spans. |
| Outcome: | The proposed model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. |
Neural Duplicate Question Detection without Labeled Training Data (D19-1)
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| Challenge: | Recent studies have used alternative methods to train neural models to duplicate question detection in community Question Answering forums. |
| Approach: | They propose two new methods for supervised question detection in community Question Answering forums . they propose weak supervision using title and body of question and automatic generation of duplicate questions . |
| Outcome: | The proposed methods can achieve better performance even without labeled data. |
Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)
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| Challenge: | Existing clarification datasets with limited annotated examples do not address ambiguous phenomena. |
| Approach: | They propose a dataset that allows users to ask clarification questions using open-domain examples. |
| Outcome: | The proposed model achieves better performance than strong baselines and provides new challenges. |
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (D19-1)
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| Challenge: | Stack Exchange has fewer than 160 user-labeled duplicates, and 25% have fewer. |
| Approach: | They propose a framework that combines sentence encoders with unlabeled data to solve the problem of duplicate question detection in Community Question Answering forums. |
| Outcome: | The proposed framework outperforms BM25, a single-view system and a supervised domain-adversarial DQD method on the CQADupStack corpus and on Stack Exchange forums. |
Multi-label Categorization of Accounts of Sexism using a Neural Framework (D19-1)
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Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma
| Challenge: | Sexism manifests in blatant as well as subtle ways, authors say . existing work on sexism classification has limitations in terms of categories used . authors: categorization of accounts of sexist behavior can aid in countering sextism . |
| Approach: | They propose a neural solution that can combine sentence representations with distributional and linguistic word embeddings. |
| Outcome: | a new method outperforms deep learning and traditional methods by an appreciable margin . the proposed method outpersforms several deep learning as well as traditional baselines by an approval margin compared to baselines . |
The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets (D19-1)
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| Challenge: | characterization scores are associated with popularity of a given short text, but are not always representative of the source. |
| Approach: | They use a dataset of tweets from 15 celebrities to quantify the extent to which a given short text is characteristic of a specific person. |
| Outcome: | The proposed model shows a statistically significant correlation between characterization scores and popularity of the associated texts for 13 of the 15 celebrities in the study. |
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts (D19-1)
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| Challenge: | Existing tools for hate speech detection and sentiment analysis cannot detect veiled offensiveness of microaggressions . linguistic subtlety of micro-aggressives has made it difficult to analyze their exact nature . |
| Approach: | They propose a typology of microaggressions based on a subset of data . they propose an objective criterion for annotation and an active-learning procedure . |
| Outcome: | The proposed typology of microaggressions is based on a subset of social media data. |
Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)
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| Challenge: | a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action . |
| Approach: | They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework . |
| Outcome: | The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com . |
Learning Invariant Representations of Social Media Users (D19-1)
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| Challenge: | Existing methods for learning to compare social media users fail to generalize to new users or even to previously known users. |
| Approach: | They propose a procedure to learn a mapping from short episodes of user activity to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. |
| Outcome: | The proposed procedure can be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. |
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (D19-1)
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| Challenge: | Despite recent attempts on computational modeling of the variation, the lack of parallel corpora of style language makes it difficult to systematically control the stylistic change and evaluate such models. |
| Approach: | They propose to use a parallel and annotated stylistic language dataset to test the effectiveness of style transfer models. |
| Outcome: | The proposed model outperforms the unsupervised models using nonparallel corpus. |
Movie Plot Analysis via Turning Point Identification (D19-1)
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| Challenge: | Using computational literary analysis, we analyze novels, plays, and screenplays for their turning points. |
| Approach: | They propose to use turning points to analyze screenplays and plot synopses as tools for analysis . they propose to build a neural network model that identifies turning points in plot synoopse . |
| Outcome: | The proposed model outperforms baselines based on state-of-the-art sentence representations and expected position of turning points. |
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (D19-1)
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| Challenge: | Existing approaches to detect suicidal ideation on social media are limited to a small group of people. |
| Approach: | They propose to use tree holes to embed words into microblogs to strengthen the sensibility of suicide-related lexicons and to use a two-layered attention mechanism to grasp intermittently changing points from individual's open blog streams. |
| Outcome: | The proposed approach can achieve over 91% accuracy with the use of suicide-oriented word embeddings and attention on a large-scale well-labelled suicide data set. |
Deep Ordinal Regression for Pledge Specificity Prediction (D19-1)
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| Challenge: | Currently, there are no publicly available annotated datasets of pledges . a novel approach to specificity prediction is needed to predict the specificity of pledged issues. |
| Approach: | They propose deep ordinal regression approaches for specificity prediction using supervised and semi-supervised settings. |
| Outcome: | The proposed methods demonstrate their utility over several baseline approaches. |
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks (D19-1)
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| Challenge: | Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. |
| Approach: | They propose a data-driven approach to goal-oriented dialogue generation which only uses a few example dialogues, none of which has to be annotated. |
| Outcome: | The proposed approach significantly improves upon baseline models and over the previous state-of-the-art model, ZSDG. |
Multi-Granularity Representations of Dialog (D19-1)
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| Challenge: | Neural models of dialog rely on generalized latent representations of language. |
| Approach: | They propose a training procedure which explicitly learns multiple representations of language at several levels of granularity. |
| Outcome: | The proposed training procedure significantly improves performance on the next utterance retrieval task using the MultiWOZ dataset and the Ubuntu dialog corpus. |
Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)
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| Challenge: | Existing methods to detect identity fraud are prone to errors and are not based on real data. |
| Approach: | They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information. |
| Outcome: | The proposed system can detect fraudsters and achieve higher recognition accuracy compared with rule-based systems. |
Hierarchy Response Learning for Neural Conversation Generation (D19-1)
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| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
| Approach: | They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model . |
| Outcome: | The proposed model can generate the responses with more appropriate content and expression. |
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (D19-1)
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| Challenge: | Existing knowledge-based open domain conversation generation models are limited by the use of unstructured knowledge texts. |
| Approach: | They propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. |
| Outcome: | The proposed system is more explainable and flexible than state-of-the-art models. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |
Towards Knowledge-Based Recommender Dialog System (D19-1)
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| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
| Approach: | They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences. |
| Outcome: | The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines. |
Structuring Latent Spaces for Stylized Response Generation (D19-1)
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| Challenge: | Existing methods for generating responses in a targeted style are limited by the lack of parallel data. |
| Approach: | They propose a method that bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. |
| Outcome: | The proposed system generates responses of the targeted style and outperforms baselines without sacrificing appropriateness. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
Unsupervised Context Rewriting for Open Domain Conversation (D19-1)
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| Challenge: | Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency. |
| Approach: | They propose a context rewriting method which explicitly rewrites the last utterance by considering context history. |
| Outcome: | The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots. |
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)
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| Challenge: | Existing models for personalized dialogues rank responses according to their semantic relevance with the given context. |
| Approach: | They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots. |
| Outcome: | The proposed model outperforms the existing model by 14.5% and 27.7% on a PERSONA-CHAT dataset. |
DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs (D19-1)
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| Challenge: | Existing work has not shown that knowledge-grounded models can zero-shot adapt to updated, unseen knowledge graphs. |
| Approach: | They propose a task to apply dynamic knowledge graphs to neural conversation models . they propose 'dyKgChat' that selects an output from two networks at each time step . |
| Outcome: | The proposed model outperforms existing knowledge-grounded conversation models in evaluation metrics. |
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)
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| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
| Approach: | They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator. |
| Outcome: | The proposed framework outperforms baseline models in a variety of experiments. |
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
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| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
| Outcome: | The proposed model scales easily with the increasing number of pre-defined slots and domains and reaches the state-of-the-art performance on the multi-domain and single domain dialogue state tracking datasets. |
Low-Resource Response Generation with Template Prior (D19-1)
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| Challenge: | Existing open domain response generation models are limited to paired data, but are less explored in real-world applications. |
| Approach: | They propose to train a neural response generation model with unpaired data and paired data as prior. |
| Outcome: | The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available. |
A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)
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| Challenge: | Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory . |
| Approach: | They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation. |
| Outcome: | The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses. |
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)
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| Challenge: | In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation. |
| Approach: | They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements. |
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation (D19-1)
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| Challenge: | Existing methods favor uninformative and non replier-specific responses due to lack of relevant information guidance. |
| Approach: | They propose to use a semi-supervised variable network to generate replier-specific responses . they use vMF as latent space to obtain stable KL performance . |
| Outcome: | The proposed model outperforms baseline models on two large conversation datasets and generates diverse and replier-specific responses. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
Variational Hierarchical User-based Conversation Model (D19-1)
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| Challenge: | Recent approaches to conversation response generation model speakers and utterances together but are too tailored to the speakers. |
| Approach: | They propose a new conversation model with a stochastic variable conditioned on the speakers and affects the context. |
| Outcome: | The proposed model outperforms existing models in generating appropriate conversation responses. |
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue (D19-1)
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| Challenge: | Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood. |
| Approach: | They use a goal-driven recommendation dialogue dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. |
| Outcome: | The proposed system can converse and recommend movies to humans without considering the task goal itself. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
How to Build User Simulators to Train RL-based Dialog Systems (D19-1)
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| Challenge: | RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive. |
| Approach: | They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators. |
| Outcome: | The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly. |
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning (D19-1)
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| Challenge: | Existing studies on video captioning focus on the association relationships between multiple modalities. |
| Approach: | They propose a video captioning model with high-order cross-modal attention (HOCA) they propose low-rank HOCA which adopts tensor decomposition to reduce the space requirement . |
| Outcome: | The proposed model captures cross-modal interaction of different modalities and reduces space requirement. |
Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach (D19-1)
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| Challenge: | Recent work on image captioning has made impressive progress . however, the results are limited and the model is difficult to train . |
| Approach: | They propose a semi-supervised framework for training an image captioning model by assigning pseudo-labels to unpaired samples via Generative Adversarial Networks. |
| Outcome: | The proposed framework is compared to baselines when the number of paired samples is scarce. |
Dual Attention Networks for Visual Reference Resolution in Visual Dialog (D19-1)
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| Challenge: | Visual dialog (VisDial) requires a dialog agent to answer a series of questions grounded in an image. |
| Approach: | They propose dual attention networks (DAN) for visual reference resolution in VisDial. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model on VisDial datasets. |
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents (D19-1)
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| Challenge: | a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific images and specific sentences. |
| Approach: | They propose algorithms that discover image-sentence relationships without explicit annotation . they experiment on seven datasets of varying difficulty . |
| Outcome: | The proposed algorithms can predict links between images and sentences without explicit multimodal annotation. |
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor (D19-1)
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Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, Mohammed (Ehsan) Hoque
| Challenge: | Humor is a unique and creative communicative behavior often displayed during social interactions. |
| Approach: | They present a dataset that allows to model multimodal language used in expressing humor using text, visual and acoustic communication. |
| Outcome: | The proposed framework opens the door to understanding multimodal language used in expressing humor. |
Partners in Crime: Multi-view Sequential Inference for Movie Understanding (D19-1)
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| Challenge: | Existing multi-view learning approaches are tested in unsupervised setups, allowing for learning of representation for monolithic data points, not sequences. |
| Approach: | They propose a neural architecture paired with a novel objective for incremental inference that integrates multi-view information for sequence prediction problems. |
| Outcome: | The proposed model outperforms previous work and strong baselines on two crime cases and speaker type tagging tasks that contribute to movie understanding. |
Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag (D19-1)
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| Challenge: | Existing models of video captioning use a network and semantics are mixed into one feature. |
| Approach: | They propose an Adaptive Semantic Guidance Network which instantiates whole video semantics to different POS-aware semantics with supervision of part of speech (POS) tag. |
| Outcome: | Extensive experiments show that the proposed model is more efficient than state-of-the-art models. |
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (D19-1)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose a framework to incorporate intent information into slot filling tasks . they use a joint model with Stack-Propagation to capture intent semantic knowledge . |
| Outcome: | The proposed model outperforms existing models on two publicly available datasets and outperformed existing models by a large margin. |
Talk2Car: Taking Control of Your Self-Driving Car (D19-1)
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| Challenge: | a long-term goal of artificial intelligence is to have an agent execute commands through natural language. |
| Approach: | They propose to use a dataset to compare commands written in natural language for self-driving cars with other datasets. |
| Outcome: | The proposed task is a challenging one and shows promising results, the authors argue . the talk2car dataset compares with similar datasets and shows that the proposed task requires additional research in natural language processing and computer vision. |
Fact-Checking Meets Fauxtography: Verifying Claims About Images (D19-1)
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| Challenge: | Recent explosion of false claims in social media has led to manual fact-checking initiatives . however, existing methods are inadequate to deal with the growing number of false content claims. |
| Approach: | They propose to model claims about images using a new dataset to examine the relationship between the image and the claim. |
| Outcome: | The proposed method improves on the baseline and will enable future research on fact-checking claims about images. |
Video Dialog via Progressive Inference and Cross-Transformer (D19-1)
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| Challenge: | Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. |
| Approach: | They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous. |
| Outcome: | The proposed method can be used to infer video dialog answers on large-scale datasets. |
Executing Instructions in Situated Collaborative Interactions (D19-1)
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Alane Suhr, Claudia Yan, Jack Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, Yoav Artzi
| Challenge: | a collaborative game with natural language instruction allows users to adapt to the system abilities by changing their language or deciding to accomplish tasks themselves. |
| Approach: | They propose a collaborative game where a user instructs a system to complete tasks, but acts alongside it. |
| Outcome: | The proposed game allows users to adapt to the system abilities by changing their language or deciding to accomplish tasks themselves. |
Fusion of Detected Objects in Text for Visual Question Answering (D19-1)
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| Challenge: | Recent neural architectures such as Transformer and BERT allow for multimodal context, which may help model the meaning of words in general and also sharpen its understanding of instances of words. |
| Approach: | They propose a neural architecture that combines vision and natural language to advance models of multimodal context. |
| Outcome: | The proposed architecture achieves the highest performance on the Visual Commonsense Reasoning benchmark and the best performance to date on the public leaderboard. |
TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)
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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
Universal Adversarial Triggers for Attacking and Analyzing NLP (D19-1)
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| Challenge: | Using adversarial triggers, a model can produce a specific prediction . adversarial attacks are useful for evaluation and interpretation . |
| Approach: | They propose a gradient-guided search over tokens that finds short adversarial triggers that successfully trigger the target prediction. |
| Outcome: | The proposed algorithm finds short trigger sequences that successfully trigger the target prediction. |
To Annotate or Not? Predicting Performance Drop under Domain Shift (D19-1)
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| Challenge: | Performance drop due to domain-shift is an endemic problem for NLP models in production. |
| Approach: | They propose to use H-divergence, reverse classification accuracy and confidence measures to predict performance drop under domain-shift without any target domain labels. |
| Outcome: | The proposed method predicts performance drops with an error rate as low as 2.15% and 0.89% for sentiment analysis and POS tagging respectively. |
Adaptively Sparse Transformers (D19-1)
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| Challenge: | Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. |
| Approach: | They propose an adaptively sparse Transformer where attention heads have flexible, context-dependent sparsity patterns. |
| Outcome: | The proposed model improves interpretability and head diversity when compared to softmax-based models on machine translation datasets. |
Show Your Work: Improved Reporting of Experimental Results (D19-1)
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| Challenge: | Current practice is to train multiple instantiations of each, choose the best model of each type, and compare their performance on held-out test data. |
| Approach: | They propose to measure expected validation accuracy as a function of computation budget . authors find comparisons where authors would have reached different conclusions if they had used more computation . |
| Outcome: | The proposed method shows that test-set performance scores alone are insufficient for drawing accurate conclusions about which model performs best. |
A Deep Factorization of Style and Structure in Fonts (D19-1)
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| Challenge: | Using a variational inference procedure, we factor each training glyph into a combination of a character-specific content embedding and a latent font-specific style variable. |
| Approach: | They propose a deep factorization model that disentangles content from style by factorizing each training glyph into a latent content embedding and a learned embeddable character. |
| Outcome: | The proposed model outperforms a strong nearest neighbors baseline and state-of-the-art discriminative model on reconstructing missing glyphs from an unknown font given only a small number of observations. |
Cross-lingual Semantic Specialization via Lexical Relation Induction (D19-1)
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| Challenge: | Semantic specialization is not available in many languages because of their incomplete or non-existent structure. |
| Approach: | They propose a method that transfers specialization from a resource-rich source language to virtually any target language. |
| Outcome: | The proposed method performs lexical simplification, dialog state tracking, and textual similarity tasks in 5 languages. |
Modelling the interplay of metaphor and emotion through multitask learning (D19-1)
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| Challenge: | Existing research suggests that metaphorical phrases are more emotionally evocative than their literal counterparts. |
| Approach: | They propose a joint model of the relationship between metaphor and emotion within a computational framework by using hard and soft parameter sharing. |
| Outcome: | The proposed model advances the state of the art in both of these tasks. |
How well do NLI models capture verb veridicality? (D19-1)
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| Challenge: | In natural language inference, contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. |
| Approach: | They propose to use a dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. |
| Outcome: | The proposed model learns to make correct inferences about veridicality in verb-complement constructions. |
Modeling Color Terminology Across Thousands of Languages (D19-1)
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| Challenge: | Existing studies on what constitutes a "basic" color term and its acquisition sequence are flawed . a pan-lingual approach may reveal general color trends more reliably than smaller datasets. |
| Approach: | They propose to operationalize and critique the Berlin and Kay color term hypotheses . they use 14 empirically-grounded computational linguistic metrics to analyze cross-linguistic data . |
| Outcome: | The proposed measures correlate strongly with the Berlin and Kay color term partition and their hypothesized universal acquisition sequence. |
Negative Focus Detection via Contextual Attention Mechanism (D19-1)
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| Challenge: | Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite. |
| Approach: | They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information. |
| Outcome: | The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art. |
A Unified Neural Coherence Model (D19-1)
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| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
| Approach: | They propose a unified coherence model that incorporates sentence grammar, inter-sentence coherent relations, and global coherency patterns into a common neural framework. |
| Outcome: | The proposed model outperforms existing models on local and global discrimination tasks and outperformed existing models by a good margin. |
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information (D19-1)
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| Challenge: | Existing methods for sentence ordering are based on pairwise strategies. |
| Approach: | They propose a topic-guided coherence modeling (TGCM) for sentence ordering that utilizes sentence vectors in a permutation-invariant manner. |
| Outcome: | The proposed model outperforms state-of-the-art models from various perspectives. |
Neural Generative Rhetorical Structure Parsing (D19-1)
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| Challenge: | Existing approaches to RST parsing use discriminative models, but these are less sample efficient and datasets are typically small. |
| Approach: | They propose a document-level RNN grammar with a bottom-up traversal order and a beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias. |
| Outcome: | The proposed model outperforms a discriminative model with the same features by 2.6 F1points and achieves comparable performance to the state-of-the-art. |
Weak Supervision for Learning Discourse Structure (D19-1)
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| Challenge: | a weak supervision approach is a promising tool for learning discourse structure for multi-party dialogue. |
| Approach: | They propose a data programming paradigm that allows a user to label training data using expert-composed heuristics and transform them into probability distributions of the class labels. |
| Outcome: | The proposed approach outperforms both deep learning and traditional ML approaches on the task of learning discourse structure for multi-party dialogue. |
Predicting Discourse Structure using Distant Supervision from Sentiment (D19-1)
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| Challenge: | Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization. |
| Approach: | They propose a method that uses document supervision to generate abundant data for RST-style discourse structure prediction by using an optimal CKY-style tree generation algorithm. |
| Outcome: | The proposed approach performs well on the more difficult task of inter-domain discourse structure prediction, but it does not match the performance of a parser trained and tested on the same dataset. |
The Myth of Double-Blind Review Revisited: ACL vs. EMNLP (D19-1)
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| Challenge: | a double-blind review system enforces author anonymity during the review period . authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Approach: | They examine how well deep learning techniques can infer authors of a paper . authors found authors can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP . |
| Outcome: | The proposed method can infer authors with 87% accuracy on ACL and 78% on EMNLP for the top 100 most prolific authors. |
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization (D19-1)
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| Challenge: | Sexual harassment is a pervasive, worldwide problem with a long history . statistics show that girls and women are put at high risk of experiencing harassment. |
| Approach: | They manually annotated sexual harassment stories with labels in dimensions of location, time, and harassers’ characteristics and applied natural language processing techniques to extract key elements at the same time. |
| Outcome: | The proposed algorithms will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, and enabling faster action to be taken. |
Identifying Predictive Causal Factors from News Streams (D19-1)
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| Challenge: | Existing word embedding techniques are not suited to learn relationships between words in different documents and contexts. |
| Approach: | They propose a new framework to uncover the relationship between news events and real world phenomena by measuring how word occurrence influences future occurrence. |
| Outcome: | The proposed framework outperforms existing methods in stock price prediction errors for 12 months and 4 years. |
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content (D19-1)
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Sepideh Mesbah, Jie Yang, Robert-Jan Sips, Manuel Valle Torre, Christoph Lofi, Alessandro Bozzon, Geert-Jan Houben
| Challenge: | Existing dictionary-based, semi-supervised learning approaches are limited by the coverage and maintainability of laymen health vocabularies. |
| Approach: | They propose a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset and generate a small set of labeled training sets. |
| Outcome: | The proposed approach matches the performance of fully-supervised approaches while requiring only 25% of training data. |
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference (D19-1)
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| Challenge: | Recent research shows textual data alone may contain enough information about users' private-attributes that they do not want to disclose such as age, gender, location, political views and sexual orientation. |
| Approach: | They propose a novel Reinforcement Learning-based Text Anonymizor which extracts a latent representation of the original text w.r.t. a given task and leverages deep reinforcement learning to learn an optimal strategy for manipulating text representations w/ the received privacy and utility feedback. |
| Outcome: | The proposed approach preserves both privacy and utility of textual data while preserving its utility. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
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| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)
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| Challenge: | Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems. |
| Approach: | They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer. |
| Outcome: | The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths . |
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (D19-1)
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| Challenge: | Existing reading comprehension datasets focus on factual and literal understanding of context paragraphs, but our dataset focuses on reading between the lines over a diverse collection of everyday narratives. |
| Approach: | They propose a large-scale dataset that requires commonsense-based reading comprehension, formulated as multiple-choice questions. |
| Outcome: | The proposed architecture improves over the baselines of existing reading comprehension datasets and shows a significant gap between machine (68.4%) and human performance (94%). |
Finding Generalizable Evidence by Learning to Convince Q&A Models (D19-1)
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| Challenge: | a system that finds the strongest supporting evidence for a given answer is proposed . a study using passage-based question-answering (QA) shows that agents select evidence that generalizes . |
| Approach: | They propose a system that finds the strongest supporting evidence for a given answer . they use passage-based question-answering (QA) as a testbed to train evidence agents . |
| Outcome: | The proposed system improves QA in a robust manner by using agent-selected evidence. |
Ranking and Sampling in Open-Domain Question Answering (D19-1)
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| Challenge: | Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing. |
| Approach: | They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph. |
| Outcome: | Experiments on three datasets show that the proposed model advances the state of the art. |
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs (D19-1)
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| Challenge: | Knowledge graph embedding (KGE) is a promising approach to knowledge graph completion. |
| Approach: | They propose a bilinear KGE model based on block circulant matrices that is non-commutative and can be modeled by matrix product. |
| Outcome: | The proposed model can be used to model composite relations on a spectrum from diagonal to full relation matrices. |
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)
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| Challenge: | Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer. |
| Approach: | They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers. |
| Outcome: | The proposed model achieves state-of-the-art performance while corresponding to more definitive answers. |
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)
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| Challenge: | Recent approaches to handle large knowledge base decompose tasks into subtasks and solve them sequentially. |
| Approach: | They propose a multi-task learning framework that resolves coreference in conversations . they propose enabling shared supervisions and type-aware entity detection model . |
| Outcome: | The proposed framework improves overall F1 score from 67% to 79% on a large-scale conversational question answering dataset. |
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels (D19-1)
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| Challenge: | Using BiPaR, we build monolingual, multilingual and cross-lingual MRC on novels. |
| Approach: | They propose a bilingual parallel novel-style machine reading comprehension dataset BiPaR . they collect 3,667 bilingual parallel paragraphs from Chinese and English novels . |
| Outcome: | The proposed dataset supports multilingual and cross-lingual reading comprehension. |
Language Models as Knowledge Bases? (D19-1)
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Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander Miller
| Challenge: | Recent advances in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. |
| Approach: | They present a method for pretraining language models on large textual corpora . they find that they can store relational knowledge and answer queries structured as "fill-in-the-blank" queries. |
| Outcome: | The proposed language models can recall factual knowledge without fine-tuning without fine tuning . the proposed models can answer queries structured as "fill-in-the-blank" cloze statements . |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
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| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)
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| Challenge: | Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages. |
| Approach: | They propose to use Unicoder to train models in one language and apply it to other languages. |
| Outcome: | Experiments show that Unicoder learns the mappings among different languages from more perspectives. |
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (D19-1)
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| Challenge: | Existing QG models suffer from a “semantic drift” problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. |
| Approach: | They propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. |
| Outcome: | The proposed method achieves state-of-the-art performance w.r.t. traditional evaluation metrics and performs best on QA-based evaluation metrics. |
Adversarial Domain Adaptation for Machine Reading Comprehension (D19-1)
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| Challenge: | Existing models for machine reading comprehension rely on large amounts of human-annotated in-domain data. |
| Approach: | They propose an unsupervised domain adaptation framework for Machine Reading Comprehension where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. |
| Outcome: | The proposed framework can be generalizable to different MRC models and datasets and can be extended to semi-supervised learning. |
Incorporating External Knowledge into Machine Reading for Generative Question Answering (D19-1)
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| Challenge: | Existing knowledge-aware QA models do not have commonsense and background knowledge to answer nontrivial questions. |
| Approach: | They propose a new neural model which exploits external knowledge to generate answers in natural language for a given question with context. |
| Outcome: | The proposed model improves answer quality over existing models without knowledge and knowledge-aware models, a study shows . state officials in Hawaii confirmed that president Barack Obama was born in the U.S. |
Answering questions by learning to rank - Learning to rank by answering questions (D19-1)
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| Challenge: | Existing approaches to answer multiple-choice questions with no supporting documents are poor performance. |
| Approach: | They propose a method which can be used to semantically rank documents extracted from Wikipedia . they propose 'semantic ranking' method that latently learns to rank documents by their importance . |
| Outcome: | The proposed model achieves state-of-the-art accuracy on two datasets: ARC Easy and Challenge. |
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension (D19-1)
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| Challenge: | Existing work on discourse-aware self-attention models for reading comprehension uses annotations . |
| Approach: | They propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder for reading comprehension on narrative texts. |
| Outcome: | The proposed model improves reading comprehension performance on narrative texts up to +3.4 Rouge-L . it also improves inter- and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations. |
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (D19-1)
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| Challenge: | Recent advances in representation learning have separated progress in both IR and MC . few studies have examined the relationship between retrieval and comprehension at different levels of granularity for development of MRS systems. |
| Approach: | They propose a simple yet effective pipeline system with consideration on hierarchical semantic retrieval at both paragraph and sentence level and their potential effects on the downstream task. |
| Outcome: | The proposed system achieves state-of-the-art on the leaderboard test sets of both FEVER and HOTPOTQA. |
PubMedQA: A Dataset for Biomedical Research Question Answering (D19-1)
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| Challenge: | PubMedQA is a biomedical question answering dataset based on PubMed abstracts . 68.1% accuracy is achieved, compared to single human performance of 78.0% . |
| Approach: | They propose a biomedical question answering dataset from PubMed abstracts . the dataset is annotated by experts and has 1k instances of QA . |
| Outcome: | The proposed model achieves 68.1% accuracy compared to human performance of 78.0% and majority-baseline of 55.2%. |
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering (D19-1)
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| Challenge: | Arras et al., 2017) suggest an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) . |
| Approach: | They propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering that maximizes the relevance of the selected sentences, minimizes overlap between selected facts, and maximizes coverage of both question and answer. |
| Outcome: | The proposed strategy improves state-of-the-art supervised QA model on two multi-hop QA datasets: AI2’s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). |
Answering Complex Open-domain Questions Through Iterative Query Generation (D19-1)
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| Challenge: | Currently, one-step retrieve-and-read question answering systems cannot answer such questions because they rarely contain retrievable clues about the missing entity. |
| Approach: | They propose a multi-step approach to retrieve relevant content with the question, then reading the paragraphs returned by the information retrieval component to arrive at the final answer. |
| Outcome: | The proposed model outperforms the best previously published model despite not using pretrained language models such as BERT. |
NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions (D19-1)
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| Challenge: | Existing studies focus on generating SQL codes from natural language questions . however, questions cover more diverse tasks including table manipulation or performance issues . |
| Approach: | They propose a task to generate pSQL codes from natural language questions . they define two new metrics suitable for the task, Canonical-BLEU and SQL-BLUE . |
| Outcome: | The proposed task generates well-formed queries on under-specified database issues. |
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (D19-1)
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| Challenge: | Existing approaches build universal paraphrasing or ranking models for whole questions . current approaches build a universal ranking model for the whole questions, which fails for complex, long-tail questions. |
| Approach: | They propose a new query generation approach based on frequent query substructures which helps rank existing query structures or build new query structures. |
| Outcome: | The proposed approach significantly outperforms existing models on two benchmark datasets. |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |
Learning to Update Knowledge Graphs by Reading News (D19-1)
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| Challenge: | Existing methods to update knowledge graphs rely on elaborately designed IE systems and domain-specific rules. |
| Approach: | They propose a novel neural network method to update knowledge graphs (KGs) they use a text-based attention mechanism to guide updating messages through KGs . |
| Outcome: | The proposed method can effectively broadcast news information to KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets. |
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing knowledge graph reasoning methods require numerous trials for path-finding and require meticulous reward engineering to fit specific datasets. |
| Approach: | They propose a plug-and-play framework that uses generative adversarial imitation learning to enhance existing RL-based methods. |
| Outcome: | The proposed framework improves existing RL-based methods while eliminating reward engineering. |
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)
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| Challenge: | Sentence matching is a key issue in natural language inference and paraphrase identification. |
| Approach: | They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer. |
| Outcome: | The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets. |
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)
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| Challenge: | Existing knowledge graphs are incomplete and lack the order of relations in paths. |
| Approach: | They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning. |
| Outcome: | The proposed method performs better than state-of-the-art methods on two benchmark datasets. |
Collaborative Policy Learning for Open Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (D19-1)
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| Challenge: | Understanding event and event-centered commonsense reasoning is crucial for natural language processing (NLP). |
| Approach: | They propose a If-Then commonsense reasoning dataset Atomic and an RNN-based Seq2Seq model to facilitate this. |
| Outcome: | The proposed model improves the accuracy and diversity of inferences compared with baseline methods. |
Asynchronous Deep Interaction Network for Natural Language Inference (D19-1)
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| Challenge: | Existing methods have framed the reasoning problem as a semantic matching task. |
| Approach: | They propose an asynchronous deep interaction network (ADIN) to deconstruct the reasoning process and implement asynchron and multi-step reasoning. |
| Outcome: | The proposed model outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail. |
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange (D19-1)
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| Challenge: | Existing methods to adjust semantics of text while preserving its style have not been investigated to the best of our knowledge. |
| Approach: | They propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. |
| Outcome: | The proposed pipeline outperforms baseline models on Yelp reviews, Amazon reviews, and news headlines in terms of its Semantic Text Exchange Score (STES) |
Query-focused Scenario Construction (D19-1)
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| Challenge: | Stronger neural network models and harder synthetic training settings are important to achieve high performance. |
| Approach: | They propose a query-based system that extracts compatible sets of events from news data . stronger neural network models and harder synthetic training settings are important to achieve high performance . |
| Outcome: | The proposed system outperforms baselines on a human-curated dataset of scenarios about real-world news topics. |
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)
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| Challenge: | Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages. |
| Approach: | They propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model to make better use of seed alignments to propagate over the entire graphs with KG-based constraints. |
| Outcome: | The proposed method can make better use of seed alignments to propagate over entire graphs with KG-based constraints. |
Designing and Interpreting Probes with Control Tasks (D19-1)
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| Challenge: | Existing studies on supervised models to predict properties from representations have shown high accuracy on a range of linguistic tasks. |
| Approach: | They propose control tasks which associate word types with random outputs to complement linguistic tasks by construction . they find that popular probes on ELMo representations are not selective . |
| Outcome: | The proposed tasks associate word types with random outputs to complement linguistic tasks. |
Specializing Word Embeddings (for Parsing) by Information Bottleneck (D19-1)
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| Challenge: | Pre-trained word embeddings contain rich syntactic and semantic information. |
| Approach: | They propose a variational information bottleneck method to nonlinearly compress word embeddings . they compress each word embedded to either a discrete tag or a continuous vector . |
| Outcome: | The proposed method can be extended to other syntactic or semantic tasks. |
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited (D19-1)
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| Challenge: | In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations. |
| Approach: | They propose to use deep contextualized word embeddings to pack information about global sentence structure into local feature representations to make the two approaches virtually equivalent in terms of accuracy and error profile. |
| Outcome: | The proposed model improves the accuracy and error profile of transition-based and graph-based dependency parsers on 13 languages. |
Semantic graph parsing with recurrent neural network DAG grammars (D19-1)
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| Challenge: | Semantic parsing is the task of mapping natural language to machine interpretable meaning representations. |
| Approach: | They propose a graph-aware sequence model that generates only well-formed graphs . their model is based on a multilingual semantic graphbank . |
| Outcome: | The proposed model yields competitive results in English and establishes the first results for German, Italian and Dutch. |
75 Languages, 1 Model: Parsing Universal Dependencies Universally (D19-1)
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| Challenge: | UDify is a multilingual multi-task model that can predict universal part-of-speech, morphological features, lemmas, and dependency trees. |
| Approach: | They evaluate UDify, a multilingual multi-task model capable of predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages. |
| Outcome: | The proposed model can predict universal part-of-speech, morphological features, lemmas, and dependency trees for all 124 treebanks across 75 languages. |
Interactive Language Learning by Question Answering (D19-1)
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| Challenge: | Existing machine reading comprehension tasks lack interactive information-seeking component of comprehension. |
| Approach: | They propose a question-asking task that asks questions in a text-based environment . they propose QAit, which uses a game generator to build models that include deep reinforcement learning agents. |
| Outcome: | The proposed task poses questions about existence, location, and attributes of objects found in environment. |
What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering (D19-1)
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| Challenge: | Multi-hop textual question answering requires combining information from multiple sentences. |
| Approach: | They propose a model that explicitly identifies the knowledge gap between a key span in the provided knowledge and the answer choices. |
| Outcome: | The proposed model outperforms existing models on the OpenBookQA dataset. |
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)
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| Challenge: | empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence . |
| Approach: | They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions. |
| Outcome: | The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism. |
Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
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| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
| Approach: | They propose a weakly-supervised framework that quantifies noises from automatically generated labels to deemphasize or fix noisy data in training. |
| Outcome: | The proposed framework can deemphasize or fix noisy data in training on low-resource languages with varying similarity to English. |
A Discrete Hard EM Approach for Weakly Supervised Question Answering (D19-1)
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| Challenge: | Existing work on question answering tasks only provide weak supervision for how the answer should be computed . weak supervision is attractive because it is relatively easy to gather, allowing for large datasets . but weak supervision complicates learning because there are many different spurious ways to derive the correct answer. |
| Approach: | They propose a method to convert question answering tasks into discrete latent variable learning problems with a precomputed set of possible solutions that contains one correct option. |
| Outcome: | The proposed approach outperforms previous methods on six QA tasks and achieves state-of-the-art on five of them. |
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts (D19-1)
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| Challenge: | gradable adjectives of size are relative, i.e., determined by the context. |
| Approach: | They propose to model how the meaning of gradable adjectives of size can be learned from visually-grounded contexts by using four tasks to determine whether an object is ‘big’ or ‘small’. |
| Outcome: | The proposed model can learn subtending the meaning of size adjectives, but their performance decreases while moving from simple to more complex tasks. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study (D19-1)
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| Challenge: | Existing studies have focused on the ability of neural models to compute and employ phrase-level features attached to a set of words, such as subject number or whquestion words. |
| Approach: | They examine whether models can represent constituent-level features, using coordinated noun phrases as a case study. |
| Outcome: | The proposed model can combine gender and gender features to drive downstream expectations, while having less success with gender agreement. |
Towards Zero-shot Language Modeling (D19-1)
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| Challenge: | a number of natural questions have been asked about the inductive biases of neural networks on core NLP tasks. |
| Approach: | They construct an informative prior for held-out languages on a task of character-level, open-vocabulary language modelling. |
| Outcome: | The proposed model outperforms baseline models with an uninformative prior in both zero-shot and few-shot settings, showing that it is imbued with universal linguistic knowledge. |
What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments (D19-1)
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| Challenge: | Explanations are central to everyday life, and are a topic of growing interest in the AI community. |
| Approach: | They propose a word-level prediction task to investigate how explanations selectively reuse information from what is being explained. |
| Outcome: | The proposed features have strong predictive power on the echoing of a word in an explanation, and enhance neural methods of generating explanations. |
Modeling Frames in Argumentation (D19-1)
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| Challenge: | In argumentation, framing is used to emphasize a specific aspect of a topic while concealing others. |
| Approach: | They propose an unsupervised method for framing arguments into non-overlapping frames . authors propose a corpus of 12, 326 debate-portal arguments organized along the frames of debates' topics . |
| Outcome: | The proposed method outperforms baselines on the argumentation task by 0.28 points. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
Evaluating adversarial attacks against multiple fact verification systems (D19-1)
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| Challenge: | Automated fact verification is progressing due to advances in modeling and availability of large datasets. |
| Approach: | They propose two scoring metrics which take into account the correctness of adversarial instances. |
| Outcome: | The proposed method and paraphrasing method have higher potency and higher resilience than baselines. |
Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials (D19-1)
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| Challenge: | Annotation quality control is critical for building reliable corpora through linguistic annotation. |
| Approach: | They propose a method to control annotation quality using two-step reason selection using a crowdsourcing platform. |
| Outcome: | The proposed method retains the annotations with satisfactory quality out of the entire annotations mixed with those of low quality. |
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite (D19-1)
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| Challenge: | Currently, machine translation is performed at the level of individual sentences, in isolation from the rest of the document. |
| Approach: | They propose a dataset that can be used as a test suite for pronoun translation . they propose an evaluation measure to differentiate good and bad pronounce translations . |
| Outcome: | The proposed dataset can be used as a test suite for pronoun translation in English . it covers multiple source languages and different pronouner errors drawn from real system translations . |
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
| Approach: | They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations. |
| Outcome: | The proposed approach outperforms previous systems on the benchmark dataset PDTB for discourse parsing. |
Weakly Supervised Multilingual Causality Extraction from Wikipedia (D19-1)
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| Challenge: | Existing methods for extracting causality knowledge from Wikipedia are lacking in this area. |
| Approach: | They propose a method for extracting causality knowledge from Wikipedia . they exploit the multilinguality of Wikipedia and the ability to translate to multiple languages . |
| Outcome: | The proposed method achieves precision and recall above 98% and 64%, respectively. |
Attribute-aware Sequence Network for Review Summarization (D19-1)
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| Challenge: | Existing review summarization systems generate summary only based on review content and neglect the authors’ attributes (e.g., gender, age, and occupation). |
| Approach: | They propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users’ characteristics into account by encoding their attributes over the words. |
| Outcome: | The proposed model outperforms existing systems on tripAtt and human evaluation by taking the authors' attributes into account and incorporating attribute embedding and word-using habits into word prediction. |
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
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| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
| Outcome: | The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers. |
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
| Approach: | They propose to enhance neural models with external knowledge to improve fidelity of generated text. |
| Outcome: | The proposed model improves on Wikipedia infobox-to-text datasets on 21 datasets. |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)
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| Challenge: | Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora. |
| Approach: | They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task. |
| Outcome: | The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task. |
NCLS: Neural Cross-Lingual Summarization (D19-1)
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| Challenge: | Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation. |
| Approach: | They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization. |
| Outcome: | The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets. |
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning (D19-1)
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| Challenge: | Conventional abstractive headline generation methods do not optimize for maximum reader attention. |
| Approach: | They propose a model that generates sensational headlines without labeled data by classifying online headlines with many comments against a summarization model. |
| Outcome: | The proposed model generates sensational headlines without labeled data. |
Concept Pointer Network for Abstractive Summarization (D19-1)
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| Challenge: | Abstractive summarization (ABS) has gained overwhelming success owing to a tremendous development of sequence-to-sequence models and its variants. |
| Approach: | They propose a concept pointer network that leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts and then points to the most appropriate choice using both the concept set and original source text. |
| Outcome: | The proposed model improves on the DUC-2004 and Gigaword datasets and human evaluation of its abstractive abilities supports the quality of the summaries produced. |
Surface Realisation Using Full Delexicalisation (D19-1)
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| Challenge: | Existing approaches to surface realisation model word ordering, morphological inflection and contraction generation are evaluated on 10 languages covered by the SR'18 shared task. |
| Approach: | They propose a modular approach which models each of these components separately and an analysis of the differences in word ordering performance across languages. |
| Outcome: | The proposed model is compared with existing models on 10 languages covered by the SR'18 shared task. |
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (D19-1)
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| Challenge: | Existing approaches to rewrite sentences with certain attributes are difficult and often result in poor content-preservation and ungrammaticality. |
| Approach: | They propose a method that uses a sequence-to-sequence model to learn attribute transfer . existing approaches try to explicitly disentangle content and attribute information . |
| Outcome: | The proposed method outperforms complex state-of-the-art systems by a large margin in sentiment modification and formality transfer tasks. |
Better Rewards Yield Better Summaries: Learning to Summarise Without References (D19-1)
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| Challenge: | Reinforcement Learning (RL)-based document summarisation systems produce state-of-the-art performance in terms of ROUGE scores, but high summaries receive low human judgement. |
| Approach: | They propose to learn a reward function from human ratings on 2,500 summaries to generate human-appealing summary. |
| Outcome: | The proposed reward function can generate human-appealing summaries without reference summary input. |
Mixture Content Selection for Diverse Sequence Generation (D19-1)
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| Challenge: | Generating diverse sequences exhibit semantically one-to-many relationships between source and target sequences. |
| Approach: | They propose to separate diversification from generation using a general plug-and-play module that wraps around and guides an existing encoder-decoder model. |
| Outcome: | The proposed method shows that diversification and generation are separate steps in the same model and that the model is robust. |
An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time) (D19-1)
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| Challenge: | Seq2Seq models for table-to-text generation have achieved remarkable progress, but modeling table representation in one dimension is inadequate. |
| Approach: | They propose to model each table cell considering other records in the same row and to enrich table’s representation by modeling each cell in context of other cells in the similar column or with historical data respectively. |
| Outcome: | The proposed model outperforms baseline and state-of-the-art models on ROTOWIRE, a benchmark dataset of NBA basketball games. |
Subtopic-driven Multi-Document Summarization (D19-1)
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| Challenge: | Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets. |
| Approach: | They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
Referring Expression Generation Using Entity Profiles (D19-1)
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| Challenge: | Existing REG systems rely on entity-specific supervised training, which means they cannot handle entities not seen during training. |
| Approach: | They propose a deep neural network model that encodes both the local context and an external profile of the entity to generate reference realizations. |
| Outcome: | The proposed model outperforms baselines on three different splits of the WebNLG dataset according to automatic and human evaluations. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
Enhancing AMR-to-Text Generation with Dual Graph Representations (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a linguistically-grounded semantic formalism that represents the meaning of a sentence as a directed graph. |
| Approach: | They propose a graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the graph. |
| Outcome: | The proposed model achieves state-of-the-art results on two AMR datasets. |
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)
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| Challenge: | Existing automated generation of articles' characteristics is inconsistent if they are generated individually. |
| Approach: | They propose a multi-task learning model with a shared encoder and multiple decoders for each task. |
| Outcome: | The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines . |
Toward a Task of Feedback Comment Generation for Writing Learning (D19-1)
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| Challenge: | Existing work on feedback comment generation has been limited . despite its usefulness, there is no publicly available dataset for research on feedback comments . |
| Approach: | They introduce a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. |
| Outcome: | The proposed task is based on a corpus of 1,900 essays with all preposition errors annotated with feedback comments. |
Improving Question Generation With to the Point Context (D19-1)
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| Challenge: | Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation. |
| Approach: | They propose to model the unstructured sentence and the structured answer-relevant relation for question generation by combining to the point context and unstructure. |
| Outcome: | Experiments show that the proposed model improves on the unstructured sentence and the structured answer-relevant relation. |
Deep Copycat Networks for Text-to-Text Generation (D19-1)
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| Challenge: | Text-to-text generation tasks require copying words from the input to the output. |
| Approach: | They propose a transformer-based pointer network for text-to-text generation which generates more abstractive summaries and a further extension of this architecture for automatic post-editing. |
| Outcome: | The proposed model outperforms existing models in text-to-text generation tasks and improves translation accuracy. |
Towards Controllable and Personalized Review Generation (D19-1)
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| Challenge: | Existing models that generate user reviews do not consider the hierarchical structure of user reviews, thus their results lack credibility and diversity. |
| Approach: | They propose a model RevGAN that automatically generates controllable user reviews . they use self-attentive recursive autoencoders, conditional discriminators, and personalized decoder . |
| Outcome: | The proposed model outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization, and human evaluations on real-world datasets. |
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (D19-1)
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| Challenge: | Abstractive summarization approaches based on Reinforcement Learning (RL) have been proposed to overcome classical likelihood maximization. |
| Approach: | They propose to use Reinforcement Learning to learn the model parameters through RL techniques to overcome classical likelihood maximization. |
| Outcome: | The proposed measures favor ROUGE with the additional property of not requiring reference summaries. |
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
| Approach: | They propose a planning-based hierarchical variational model that plans a sequence of groups and then realizes each sentence conditioned on the planning result and the previously generated context. |
| Outcome: | The proposed model outperforms state-of-the-art models in long and diverse text generation. |
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (D19-1)
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| Challenge: | Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. |
| Approach: | They propose a new approach to rewriting sentences to a target style in the absence of parallel style corpora by exploiting the Transformer. |
| Outcome: | The proposed method outperforms state-of-the-art systems across 5 datasets on sentiment, gender and political slant transfer. |
An Entity-Driven Framework for Abstractive Summarization (D19-1)
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| Challenge: | Popular neural summarization models produce incoherent and unfaithful summaries . however, their outputs are often incohérent and incoerent . |
| Approach: | They propose a system for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on New York Times and CNN/Daily Mail datasets. |
Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
| Approach: | They propose a neural model for single-document summarization based on joint extraction and syntactic compression. |
| Outcome: | The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical. |
Domain Adaptive Text Style Transfer (D19-1)
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| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
Let’s Ask Again: Refine Network for Automatic Question Generation (D19-1)
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Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran
| Challenge: | Existing AQG models produce incomplete questions which look like incomplete drafts with scope for refinement. |
| Approach: | They propose a method which mimics the human process of generating questions by first creating an initial draft and then refining it. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three datasets and improves on fluency and answerability metrics. |
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization (D19-1)
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| Challenge: | despite recent advances in neural summarization systems, the underlying logic behind the improvements remains unexplored. |
| Approach: | They define three sub-aspects of summarization: position, importance, diversity . position exhibits substantial bias in news articles, but not with academic papers . |
| Outcome: | evaluators found that position bias is not present in academic papers and meeting minutes . elucidation provides useful lessons on analyzing summarization datasets . |
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction (D19-1)
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| Challenge: | a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and there are pervasive gaps in the gold-standard targets. |
| Approach: | They examine the composition and quality of test sets for five different languages . they suggest future research avoids drawing conclusions from quantitative results . |
| Outcome: | The results show that a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and there are gaps in the gold-standard targets. |
Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set (D19-1)
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| Challenge: | Using development sets for low-resource training is often more effective . however, some studies show that early stopping can overestimate performance . |
| Approach: | They find that early stopping on a development set is more effective than using all available data for training. |
| Outcome: | The proposed model overestimates accuracy over languages and tasks by 1.4% compared to a more realistic set of training epochs. |
Synchronously Generating Two Languages with Interactive Decoding (D19-1)
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| Challenge: | Experimental results show that multilingual NMT models handle multiple language pairs in one model. |
| Approach: | They propose an interactive approach to translate a source language into two different languages simultaneously and interactively. |
| Outcome: | The proposed approach improves on IWSLT and WMT datasets. |
On NMT Search Errors and Model Errors: Cat Got Your Tongue? (D19-1)
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| Challenge: | We show that at the root of the problem of empty translations lies an inherent bias towards shorter translations. |
| Approach: | They propose an exact inference procedure for neural sequence models based on beam search and depth-first search. |
| Outcome: | The proposed procedure finds that beam search fails to find the best model scores . the results show that the model often prefers an empty translation . |
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding (D19-1)
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| Challenge: | a new study examines temporal commonsense and compares it to human performance on a dataset . a previous study focused on duration, frequency, stationarity and ordering, but not all aspects of temporal similarity have been studied. |
| Approach: | They define five classes of temporal commonsense and use crowdsourcing to develop a new dataset that serves as a test set. |
| Outcome: | The proposed dataset shows that the best current methods are far behind human performance by 20%. |
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization (D19-1)
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| Challenge: | Existing models are not good at distinguishing distractor sentences which look related but do not answer the question. |
| Approach: | They propose a method to regularize question answering models by maximizing mutual information among passages, questions, and answers. |
| Outcome: | The proposed model achieves state-of-the-art on the Adversarial-SQuAD dataset. |
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
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| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG (D19-1)
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| Challenge: | a recent study has improved the state-of-the-art on common-sense reasoning benchmarks . a san francisco-based approach to common-ense reasoning is challenging . |
| Approach: | They propose to use common-sense reasoning benchmarks to test machine learning's common-sentence inference task SWAG to test common-mind systems. |
| Outcome: | a new study shows that improved performance on common-sense reasoning benchmarks is genuine . the proposed task is more difficult than the current one, but it is more efficient than the previous one. |
Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)
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| Challenge: | Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses) |
| Approach: | They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses. |
| Outcome: | The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation. |
Multi-Task Learning with Language Modeling for Question Generation (D19-1)
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| Challenge: | Existing work on answer-aware questions generates a sentence and answer span as input . previous work on QG was mainly tackled by rule-based approach and neural-based one . |
| Approach: | They propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. |
| Outcome: | The proposed model improves on SQuAD and MARCO datasets and human evaluation proves it. |
Autoregressive Text Generation Beyond Feedback Loops (D19-1)
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| Challenge: | Autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. |
| Approach: | They combine a latent state space model with a CRF observation model to investigate the state evolution of a hidden state trajectory. |
| Outcome: | The proposed model performs better on unconditional sentence generation compared to baselines while avoiding some prototypical failure modes. |
The Woman Worked as a Babysitter: On Biases in Language Generation (D19-1)
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| Challenge: | a systematic study of biases in natural language generation (NLG) is presented . a study of language models in NLG is conducted by examining language models. |
| Approach: | They propose a systematic study of biases in natural language generation by analyzing text generated from prompts that contain mentions of different demographic groups. |
| Outcome: | The proposed method reveals biases in natural language generation (NLG) by analyzing text generated from demographic prompts. |
On the Importance of Delexicalization for Fact Verification (D19-1)
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| Challenge: | Neural networks (NNs) perform state-of-the-art (SOA) performance in many complex tasks. |
| Approach: | They investigate the importance that a model assigns to various aspects of data . they experiment with two strategies of masking to mitigate this dependence on lexicalized information . |
| Outcome: | The proposed model improves on the in-domain dataset by 10% compared to the fully lexicalized model. |
Towards Debiasing Fact Verification Models (D19-1)
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Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, Regina Barzilay
| Challenge: | Prior research has shown that data collection methods that use crowdsourcing introduce idiosyncratic biases that impact performance in unexpected ways. |
| Approach: | They propose a method to regularize the training data to avoid idiosyncrasies in the datasets that are used for fact verification. |
| Outcome: | The proposed model outperforms the existing model on the FEVER dataset, achieving 61.7% of the baseline. |
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks (D19-1)
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| Challenge: | Existing models ignore conflict opinions because they are sparse in the datasets. |
| Approach: | They propose a multi-label classification model with dual attention mechanism to address these problems by excluding conflict opinions from existing models. |
| Outcome: | The proposed model addresses the problem of exclusion of conflict opinions from the datasets. |
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis (D19-1)
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| Challenge: | Existing deep neural network models such as LSTM and tree-LSTM have a bias problem where the words in the tail of a sentence are more heavily emphasized than those in the header. |
| Approach: | They propose a capsule tree-LSTM model that uses dynamic routing to build sentence representations by assigning different weights to nodes according to their contributions to prediction. |
| Outcome: | The proposed model improves on the Stanford Sentiment Treebank and EmoBank datasets. |
A Label Informative Wide & Deep Classifier for Patents and Papers (D19-1)
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| Challenge: | Existing methods for classification of patents and papers are manual and limited . et al., a chinese research team has developed a label-informative classification model . |
| Approach: | They propose a label-informative classifier based on the Wide & Deep structure . they train on millions of patents and transfer to papers by developing distant-supervised training set and domain-specific features. |
| Outcome: | The proposed model performs comparable to the state-of-the-art model used in industry on patents and papers. |
Text Level Graph Neural Network for Text Classification (D19-1)
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| Challenge: | Recent researches have explored graph neural network (GNN) techniques on text classification, but they are faced with the problems of fixed corpus level graph structure which don’t support online testing and high memory consumption. |
| Approach: | They propose a graph neural network model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. |
| Outcome: | The proposed model outperforms existing models on several text classification datasets even with consuming less memory. |
Semantic Relatedness Based Re-ranker for Text Spotting (D19-1)
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| Challenge: | Existing approaches to text spotting are limited by semantic similarity, but they can be useful for other tasks. |
| Approach: | They propose a neural approach to learn semantic relatedness from existing sentences. |
| Outcome: | The proposed approach outperforms existing approaches when applied to a text spotting task. |
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)
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| Challenge: | Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification. |
| Approach: | They propose a method that uses pretrained word embeddings to predict text classification . they use unlabeled data to build a classifier, and use early-stopping to improve performance . |
| Outcome: | The proposed method outperforms self-training and co-training frameworks on unlabeled data. |
Visual Detection with Context for Document Layout Analysis (D19-1)
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| Challenge: | a challenge in scientific literature mining is the difficulty of extracting high-quality text from formatted PDFs. |
| Approach: | They propose a method to visually segment key regions of scientific articles using object detection augmented with contextual features. |
| Outcome: | The proposed method improves the accuracy of the proposed method and the speed of the dataset. |
Evaluating Topic Quality with Posterior Variability (D19-1)
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| Challenge: | Probabilistic topic models such as latent Dirichlet allocation (LDA) are widely used for NLP tasks which require the extraction of latent themes. |
| Approach: | They propose to measure topic quality using the variability of posterior distributions of probabilistic topic models. |
| Outcome: | The proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. |
Neural Topic Model with Reinforcement Learning (D19-1)
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| Challenge: | Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models. |
| Approach: | They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. |
| Outcome: | The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. |
Modelling Stopping Criteria for Search Results using Poisson Processes (D19-1)
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| Challenge: | Document retrieval systems often return large sets of documents, especially when applied to large collections. |
| Approach: | They propose a method that predicts the rate at which relevant documents occur using a Poisson process and allows a user to specify a minimum desired level of recall to achieve. |
| Outcome: | The proposed method is compared with previous methods on a public dataset and compares it with existing methods. |
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (D19-1)
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| Challenge: | Existing test collections provide only document-level relevance judgments, and documents exceed the length that BERT was designed to handle. |
| Approach: | They propose to aggregate sentence-level evidence to rank news articles using BERT . they also leverage passage-level relevance judgments available in other domains to fine-tune BERT models that capture cross-domain notions of relevance. |
| Outcome: | The proposed model aggregates sentence-level evidence to rank documents on three standard test collections. |
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval (D19-1)
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| Challenge: | Existing studies do not investigate the effectiveness of MT metrics in predicting performance of downstream IR models. |
| Approach: | They examine the relationship between MT performance and IR quality in a CLIR-based system . they find that the choice of IR collection can significantly affect MT tuning decisions . |
| Outcome: | The proposed model can predict CLIR performance better from MT quality, the authors show . the proposed model is based on a BLEU-based model with a bag of words constraint . |
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space (D19-1)
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| Challenge: | Word embeddings have a notable property that word relationships can exist as linear substructures in the embeddable space. |
| Approach: | They propose an alternative way in which downstream models might learn these relationships: orthogonal and linear transformations. |
| Outcome: | The proposed model can learn such relationships as geometric translations, but there is no evidence that it is exclusively accurate. |
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. |
| Approach: | They propose to integrate gloss knowledge into supervised neural networks for Word Sense Disambiguation (WSD) this paper proposes to fine-tune a pre-trained BERT model and achieve new state-of-the-art results on WSD task. |
| Outcome: | The proposed model achieves state-of-the-art on the word Sense Disambiguation (WSD) task. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
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| Challenge: | Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations. |
| Approach: | They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL. |
| Outcome: | The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction. |
Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling (D19-1)
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| Challenge: | Existing definition modeling methods do not utilize lexical semantic relations between defined words and defining words. |
| Approach: | They propose definition modeling methods that use lexical semantic relations . they use unsupervised pattern-based word-pair embeddings that represent semantic relations of word pairs . |
| Outcome: | The proposed methods improve definition generation and learning embeddings from definitions. |
Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja (D19-1)
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| Challenge: | Existing knowledge of Korean and Chinese is based on cultural and historical reasons. |
| Approach: | They propose a method for improving Korean word representations using additional linguistic annotation by leveraging the fact that Hanja is closely related to Chinese. |
| Outcome: | The proposed approach improves representations on a novel Korean news headline generation task. |
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (D19-1)
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| Challenge: | Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. |
| Approach: | They propose to use a Lexical Knowledge Base to capture syntagmatic relations to enable knowledge-based WSD systems to achieve a new state of the art. |
| Outcome: | The proposed resource captures syntagmatic relations and is the first large-scale manually-curated resource of this kind made available to the community. |
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
Fine-tune BERT with Sparse Self-Attention Mechanism (D19-1)
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| Challenge: | Existing sparse self-attention fine-tuning models have been used to improve sentiment analysis, question answering, and natural language inference tasks. |
| Approach: | They propose a Sparse Self-Attention Fine-tuning model which integrates sparsity into self-attention mechanism to enhance the fine-tune performance of BERT. |
| Outcome: | The proposed model outperforms the baseline models on sentiment analysis, question answering, and natural language inference tasks and is able to interpret the input better. |
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
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| Challenge: | Existing approaches to improve supervised labeling with noisy training data do not take the input features into account or they need to learn the noise modeling from scratch. |
| Approach: | They propose to cluster training data using input features and compute different confusion matrices for each cluster. |
| Outcome: | The proposed model improves on low-resource named entity recognition settings in several languages, compared with other models which do not take the input features into account or need to learn noise modeling from scratch. |
A Multi-Pairwise Extension of Procrustes Analysis for Multilingual Word Translation (D19-1)
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| Challenge: | Existing approaches to multilingual word embeddings require a k-way dictionary. |
| Approach: | They propose a novel approach to simultaneously representing multiple languages in a common space by using a pairwise bilingual dictionary. |
| Outcome: | The proposed approach requires only pairwise bilingual dictionaries that are much easier to construct. |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
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| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)
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| Challenge: | Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures. |
| Approach: | They propose differentiable neural architecture search methods for natural language processing . they remove the softmax-local constraint and apply it to named entity recognition . |
| Outcome: | The proposed method outperforms strong baselines on the language modeling task. |
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets (D19-1)
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| Challenge: | Existing methods for knowledge graph completion require large batch sizes and memory constraints. |
| Approach: | They combine occurrences of entity-relation pairs to construct a joint learning model using a dataset containing 2 million entities and combine them to increase the quality of sampled negatives. |
| Outcome: | The proposed model outperforms the baseline model on a dataset containing 2 million entities by 2.8% absolute on hits@1. |
Single Training Dimension Selection for Word Embedding with PCA (D19-1)
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| Challenge: | Existing methods for word embeddings are limited by the definition of 'low' dimensionality, which is often used to train word embeds into low dimensional continuous vector space. |
| Approach: | They propose a method to select the number of dimensions for word embeddings using PCA. |
| Outcome: | The proposed method trains one embedding with a generous upper bound (e.g. 1,000) of dimensions and then removes the lesser dimensions one at a time while recording the embeddables’ performance on language tasks. |
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text (D19-1)
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| Challenge: | Variational Autoencoders are powerful language models and effective representation learning frameworks. |
| Approach: | They propose a fix for posterior collapse which improves held-out likelihood, reconstruction and latent representation learning . |
| Outcome: | The proposed fix significantly improves held-out likelihood, reconstruction, and latent representation learning compared with previous state-of-the-art methods. |
SciBERT: A Pretrained Language Model for Scientific Text (D19-1)
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| Challenge: | SciBERT is a pretrained language model based on BERT to improve performance on scientific NLP tasks. |
| Approach: | They propose a pretrained language model based on BERT to improve NLP performance . they evaluate on sequence tagging, sentence classification and dependency parsing . |
| Outcome: | The proposed model improves on sequence tagging, sentence classification and dependency parsing tasks with datasets from a variety of scientific domains. |
Humor Detection: A Transformer Gets the Last Laugh (D19-1)
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| Challenge: | Existing methods to identify humor in text have been limited to identifying humor in the text. |
| Approach: | They propose a model that learns to identify humorous jokes based on Reddit ratings, and employ a Transformer architecture to learn from sentence context. |
| Outcome: | The proposed model outperforms previous work on humor identification tasks with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset. |
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training (D19-1)
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| Challenge: | In recent years, neural network models have grown dramatically in terms of number of parameters, so exchanging gradients during data-parallel training is costly in terms both of bandwidth and time. |
| Approach: | They propose to combine the compressed global gradient with the local gradient to restore Transformer convergence while RNNs converge faster. |
| Outcome: | The proposed method restores transformer convergence while RNNs converge faster. |
Small and Practical BERT Models for Sequence Labeling (D19-1)
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| Challenge: | Existing models for morphosyntactic tagging have focused on building separate models for each language or for a small group of related languages. |
| Approach: | They propose a scheme to train a single multilingual sequence labeling model that is small and fast enough to run on a CPU. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages and low-level models on codemixed inputs. |
Data Augmentation with Atomic Templates for Spoken Language Understanding (D19-1)
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| Challenge: | Existing methods to enlarge SLU data require large amounts of labelled data. |
| Approach: | They propose a data augmentation method with atomic templates for Spoken Language Understanding which generates atomic exemplars from atomic template. |
| Outcome: | The proposed method improves on a DSTC 2&3 dataset which is a domain adaptation setting of SLU. |
PaLM: A Hybrid Parser and Language Model (D19-1)
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| Challenge: | Recent language models have shown strong data-fitting performance, but do not explicitly encode any notion of structural information. |
| Approach: | They propose a hybrid parser and neural language model that adds an attention layer over text spans in the left context. |
| Outcome: | The proposed model outperforms baseline models on language modeling and provides syntactically-informed representations of the context. |
A Pilot Study for Chinese SQL Semantic Parsing (D19-1)
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| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
Global Reasoning over Database Structures for Text-to-SQL Parsing (D19-1)
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| Challenge: | Existing semantic parsers only select a set of database constants at training time . current models only consider local information, not global ones . |
| Approach: | They propose a semantic parser that globally reasons about the structure of the query to make a more contextually-informed selection of database constants. |
| Outcome: | The proposed model increases accuracy from 39.4% to 47.4% on a zero-shot semantic parsing dataset with complex databases. |
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis (D19-1)
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| Challenge: | despite its practical advantages, transductive learning is underexplored in natural language processing . despite the simplicity of the technique, it is understudied in natural languages . |
| Approach: | They conduct an empirical study of transductive learning for neural models . they fine-tune language models on an unlabeled test set to obtain test-set-specific word representations. |
| Outcome: | The proposed method improves state-of-the-art neural models in syntactic and semantic tasks. |
Efficient Sentence Embedding using Discrete Cosine Transform (D19-1)
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| Challenge: | Modern NLP systems rely on word embeddings as input units to encode statistical semantic and syntactic properties of words. |
| Approach: | They propose to use discrete cosine transform to compress word sequences in order-preserving manner. |
| Outcome: | The proposed model preserves syntactic information in semantic probing tasks . it is comparable to vector averaging but mediocre in performance. |
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection (D19-1)
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| Challenge: | Existing structured prediction tasks target nested and overlapping events . a new structured prediction model is proposed that uses a relation graph to detect overlapping and nesting events. |
| Approach: | They propose a search-based neural network structured prediction model that treats the task as a searching problem on a relation graph of trigger-argument structures. |
| Outcome: | The proposed model performs comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of syntactic and hand-engineered features. |
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification (D19-1)
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| Challenge: | Existing work on adversarial data generation focuses on English . Existing multilingual datasets show effectiveness of deep, multilingual pre-training . |
| Approach: | They propose a dataset of 23,659 human translated PAWS evaluation pairs in six languages . they show the effectiveness of deep, multilingual pre-training while leaving considerable headroom . |
| Outcome: | The proposed model shows that multilingual training and evaluation regimes are more accurate than previous models. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |
Emergent Linguistic Phenomena in Multi-Agent Communication Games (D19-1)
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| Challenge: | a recent study examines the behavior of linguistic agents in a community-level setting . a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed ones . |
| Approach: | They propose a multi-agent communication framework for studying linguistic phenomena at the community level. |
| Outcome: | The proposed framework can reproduce complex linguistic behavior observed in natural language . it can be used to study interactions between perceptually-enabled agents . |
TalkDown: A Corpus for Condescension Detection in Context (D19-1)
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| Challenge: | condescending language use can bring dialogues to an end and disrupt healthy communities. |
| Approach: | They propose a model that uses a language-only model to model condescending linguistic acts in context. |
| Outcome: | a new model of condescending language use improves performance and motivates techniques . the model can estimate condescension rates in various online communities and relate these differences to community norms . |
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization (D19-1)
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| Challenge: | Recent work on topic-focused summarization has focused on generic summarizing due to the size of the available datasets. |
| Approach: | They propose a method to study content selection in topic-focused summarization by narrowing the scope to a sentence-level task. |
| Outcome: | The proposed method can generate a large-scale summary cloze dataset from Wikipedia. |
Text Summarization with Pretrained Encoders (D19-1)
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| Challenge: | Existing pretraining languages such as ELMo and GPT have advanced a wide range of tasks. |
| Approach: | They propose a novel document-level encoder based on BERT which can express the semantics of a document and obtain representations for its sentences. |
| Outcome: | The proposed model achieves state-of-the-art in extractive and abstractive settings. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
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| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
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| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)
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| Challenge: | Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. |
| Approach: | They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them. |
| Outcome: | The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations. |
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs (D19-1)
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| Challenge: | Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations. |
| Approach: | They propose to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. |
| Outcome: | The proposed model is highly tractable on WikiTableQuestions and WikiSQL datasets. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |
Core Semantic First: A Top-down Approach for AMR Parsing (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a semantic formalism that encodes the meaning of a sentence as a rooted labeled directed graph. |
| Approach: | They propose a scheme for parsing text into its Abstract Meaning Representation (AMR) using Graph Spanning based Parsing. |
| Outcome: | The proposed scheme achieves state-of-the-art on the latest AMR sembank and no heuristic graph re-categorization is adopted. |
Don’t paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing (D19-1)
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| Challenge: | a major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms . crowdsourcing and crowdsourcing have been used to generate pseudo-language paired with logical form . however, this data collection method often leads to low performance on real data . |
| Approach: | They propose a method that uses crowdsourcing to map language utterances to logical forms . they quantify the effects of mismatches between the true and induced distributions . |
| Outcome: | The proposed method leads to 70.6 accuracy on the true distribution, compared to 51.3 in paraphrase-based data collection. |
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding. |
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network (D19-1)
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| Challenge: | Existing models for named entity recognition (NER) lack word boundaries information, which is a major barrier to developing a high performance named entity system. |
| Approach: | They propose a Chinese named entity recognition system with word boundaries information . they use word-level representations and character-level models to integrate lexical knowledge into Chinese NER . |
| Outcome: | The proposed model outperforms the state-of-the-art model and achieves a speed of up to 15 times faster than the SOTA model. |
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction (D19-1)
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| Challenge: | Existing studies on DS-based relation extraction (RE) methods focus on handling label noise, but other factors may have been overlooked. |
| Approach: | They propose a method to automatically adjust DS-RE models to a shifted label distribution problem . they find this problem exists in real-world DS datasets and can be overcome . |
| Outcome: | The proposed method achieves consistent performance gains on DS-trained models with an up to 23% relative F1 improvement, which verifies their assumptions. |
Easy First Relation Extraction with Information Redundancy (D19-1)
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| Challenge: | Existing relation extraction models make decisions globally using integer linear programming . Existing approaches require time and memory to encode redundant information for ILP . |
| Approach: | They propose an easy first approach for relation extraction with information redundancies embedded in local sentence extractors to resolve conflict decisions with domain and uniqueness constraints. |
| Outcome: | The proposed approach outperforms both ILP and neural network-based methods in relation extraction (RE) studies have shown that the proposed approach improves the efficiency and accuracy of RE models. |
Dependency-Guided LSTM-CRF for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the most important and fundamental tasks in natural language processing (NLP). |
| Approach: | They propose a dependency-guided model to encode dependency trees and capture their properties for named entity recognition. |
| Outcome: | The proposed model improves named entity recognition performance on standard datasets. |
Cross-Cultural Transfer Learning for Text Classification (D19-1)
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| Challenge: | a large dataset is required to achieve competitive performance in most natural language tasks. large datasets are expensive, time consuming, and error-prone. |
| Approach: | They propose a transfer-learning framework that leverages bilingual corpora for natural language text classification using no task-specific data. |
| Outcome: | The proposed framework can achieve good performance on formality classification and sarcasm detection tasks without any task-specific labeled data. |
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification (D19-1)
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| Challenge: | supervised learning models perform poorly at low-shot tasks for which little labeled data is available for training. |
| Approach: | They propose to combine a bag-of-words embedding approach and a context-aware method to improve low-shot text classification. |
| Outcome: | The proposed method improves low-shot text classification with pre-training and rationales . the simple bag-of-words approach is the clear top performer when there are few training instances or less . |
ProSeqo: Projection Sequence Networks for On-Device Text Classification (D19-1)
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| Challenge: | ProSeqo is a novel on-device sequence model for text classification . it uses dynamic recurrent projections without the need to store or look up pre-trained embeddings. |
| Approach: | They propose a novel on-device sequence model for text classification using recurrent projections that uses dynamic recursion projections without the need to store or look up any pre-trained embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art neural and on-device approaches for short and long text classification tasks while maintaining low memory footprint and high accuracy. |
Induction Networks for Few-Shot Text Classification (D19-1)
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| Challenge: | Recent studies have used meta-learning to simulate the few-shot task . however, this sample-wise comparison may be severely disturbed by the various expressions in the same class. |
| Approach: | They propose a meta-learning-based induction network to learn a generalized class-wise representation of each class in a support set. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on a sentiment and dialogue intent datasets. |
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach (D19-1)
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| Challenge: | 0Shot-TC is a challenging NLU problem to which little attention has been paid by the research community. |
| Approach: | They propose to use a standardized evaluation system to classify text snippets without seeing task specific training data. |
| Outcome: | The proposed model is based on a set of standardized evaluations and state-of-the-art baselines. |
A Logic-Driven Framework for Consistency of Neural Models (D19-1)
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| Challenge: | Recent advances in NLP have improved performance on benchmarks such as GLUE . however, tracking performance on a leaderboard is not sufficient to characterize model quality . |
| Approach: | They propose a framework for constraining neural models using logic rules to regularize them away from inconsistency. |
| Outcome: | The proposed framework can be used on natural language inference and is compatible with off-the-shelf learning schemes without model redesign. |
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites (D19-1)
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| Challenge: | Currently, standard methods for style transfer have several significant problems. |
| Approach: | They propose to take BLEU between input and human-written reformulations into consideration for benchmarks. |
| Outcome: | The proposed architectures outperform state-of-the-art in style transfer metric on human-written reformulations and take BLEU between input and output into consideration for benchmarks. |
Implicit Deep Latent Variable Models for Text Generation (D19-1)
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| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
| Approach: | They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior . |
| Outcome: | The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. |
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach (D19-1)
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| Challenge: | Existing methods for text emotion distribution learning require a large amount of training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. |
| Approach: | They propose a meta-learning approach to learn text emotion distributions from a small sample using tensor decomposition to capture contextual semantic similarity. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a widely used EDL dataset. |
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation (D19-1)
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| Challenge: | Existing evaluation methods for natural language generation are inadequate . distinguishing machine-generated text is challenging even for human evaluators . |
| Approach: | They compare human-based evaluators with automated evaluation procedures . they find human evaluers do not correlate well with discriminative evalators . |
| Outcome: | The proposed evaluation methods are compared with a dozen state-of-the-art generators for online product reviews. |
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (D19-1)
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| Challenge: | Existing methods for finding similar sentences require multiple inferences . a modern GPU requires 65 hours to find the most similar pair in 10,000 sentences . |
| Approach: | They propose a modification of the pretrained BERT network that uses siamese and triplet networks to derive semantically meaningful sentence embeddings. |
| Outcome: | The proposed method outperforms existing methods on sentence-pair regression tasks. |
Learning Only from Relevant Keywords and Unlabeled Documents (D19-1)
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| Challenge: | Existing methods for document classification are limited due to labeling and privacy concerns. |
| Approach: | They propose a super-vised text classification framework that provides keywords as a hint for classifying a document to a target class. |
| Outcome: | The proposed framework is simple to implement and has flexible choices of models, e.g., linear models or neural networks. |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |
Dialog Intent Induction with Deep Multi-View Clustering (D19-1)
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| Challenge: | Existing work assumes that dialog intents are expressed in query utterances and captured in the rest of the dialog. |
| Approach: | They propose a dialog intent induction task and propose alternating-view k-means for clustering . they split a conversation into two independent views and exploit multi-view clustering techniques . |
| Outcome: | The proposed approach can induce better dialog intent clusters than state-of-the-art clustering methods. |
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction (D19-1)
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| Challenge: | Recent studies have shown that locality sensitive hashcodes are useful for biomedical relation extraction tasks. |
| Approach: | They propose to optimize locality sensitive hashcode representations in a nearly unsupervised manner . they use only data points, but not their class labels, for learning . |
| Outcome: | The proposed approach improves accuracy from training to test sets, and the data points are only used for learning . |
Auditing Deep Learning processes through Kernel-based Explanatory Models (D19-1)
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| Challenge: | Existing nonlinearity of deep learning models can be a major drawback . ethical accountability of such systems is becoming a crucial issue . |
| Approach: | They propose to use Layerwise Relevance Propagation to trace back connections between linguistic properties of input instances and system decisions. |
| Outcome: | The proposed model evaluates the transparency and coherence of analogy-based explanations modeling an audit stage for the system. |
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation (D19-1)
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| Challenge: | Existing approaches to train variational autoencoders (VAEs) have been proposed to alleviate the posterior collapse issue in NLP tasks. |
| Approach: | They propose to introduce a mutual information term between the input and its latent variable to regularize the objective of the VAE. |
| Outcome: | The proposed model performs better on three benchmark datasets and is comparable to state-of-the-art models. |
Sampling Bias in Deep Active Classification: An Empirical Study (D19-1)
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| Challenge: | Existing studies on active learning identify sampling bias in large datasets . cost and time needed for labeling and model training are bottlenecks preventing new and/or better models from being trained . |
| Approach: | They propose to use active learning to identify representative data samples for training . they propose to create tiny datasets that can be used for cheap training if needed . |
| Outcome: | The proposed model outperforms the state-of-the-art on active text classification using small representative datasets with active learning. |
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases (D19-1)
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| Challenge: | Recent advances in neural models exploit dataset-specific patterns that do not generalize well to out-of-domain or adversarial settings. |
| Approach: | They propose to train a model to be more robust to domain shift if it has prior knowledge of dataset biases. |
| Outcome: | The proposed model can be more robust to domain shift if it has prior knowledge of dataset biases. |
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (D19-1)
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Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli
| Challenge: | Recent work has exposed the vulnerabilities of neural NLP models, e.g. with small, semantically invariant input alterations. |
| Approach: | They propose to model text classification under synonym replacements or character flip perturbations and then use a formal model verification method to verify its robustness. |
| Outcome: | The proposed models show little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries. |
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (D19-1)
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| Challenge: | Selective rationalization is a common mechanism to ensure that predictive models reveal how they use any available features. |
| Approach: | They propose a co-operative method which uses introspection to explicitly predict and incorporate the outcome into the selection process. |
| Outcome: | The proposed model maintains high predictive accuracy and leads to comprehensive rationales. |
Experimenting with Power Divergences for Language Modeling (D19-1)
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| Challenge: | Language models are an important component in many NLP tasks, where they provide prior knowledge on the language used. |
| Approach: | They propose to use power divergences to prioritize learning on frequent or rare words . they use a sample-based objective to approximate a softmax and noise-constrained estimate . |
| Outcome: | The proposed power divergences can be used to prioritize learning on the frequent or rare words and lead to general performance improvements. |
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
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| Challenge: | Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding. |
| Approach: | They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. |
| Outcome: | The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model. |
Certified Robustness to Adversarial Word Substitutions (D19-1)
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| Challenge: | State-of-the-art NLP models can be fooled by adversaries that apply seemingly innocuous label-preserving transformations to input text. |
| Approach: | They propose to train models that are provably robust to all word substitutions in a family of label-preserving transformations that can be replaced with a similar word without changing the original sentiment. |
| Outcome: | The proposed models achieve 75% adversarial accuracy on both sentiment analysis and natural language inference on IMDB and SNLI compared to models trained normally and ones trained with data augmentation. |
Visualizing and Understanding the Effectiveness of BERT (D19-1)
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| Challenge: | Language model pre-training, such as BERT, has achieved strong performance in many NLP tasks. |
| Approach: | They propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets. |
| Outcome: | The proposed model improves performance and generalization capability across tasks. |
Topics to Avoid: Demoting Latent Confounds in Text Classification (D19-1)
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| Challenge: | Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. |
| Approach: | They propose a method that represents latent topical confounds and a model which “unlearns” confounding features by predicting both the label of the input text and the confound. |
| Outcome: | The proposed model generalizes better and learns features indicative of the writing style rather than the content. |
Learning to Ask for Conversational Machine Learning (D19-1)
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| Challenge: | Empirical analysis across three domains shows that learned question-asking strategies expedite classifier training by asking appropriate questions at different points in the learning process. |
| Approach: | They propose a reinforcement learning framework where the learner’s actions correspond to question types and the reward for asking a question is based on how the teacher’s response changes performance of the resulting machine learning model. |
| Outcome: | The proposed framework outperforms a random policy on learning classification tasks, but the dialog looks contrived from a human perspective. |
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training (D19-1)
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| Challenge: | Code-switching (CS) is a linguistic phenomenon defined as "the alternation of two languages within a single discourse, sentence or constituent." |
| Approach: | They propose an ASR-motivated evaluation setup which is decoupled from an ASL system and the choice of vocabulary . they propose a discriminative training approach which works better than generative language modeling . |
| Outcome: | The proposed evaluation setup is better than generative language modeling, the authors show . the proposed setup is decoupled from an ASR system and the choice of vocabulary . |
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (D19-1)
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| Challenge: | Current approaches extract portions of web text as input to Sequence-to-Sequence models . a problem is generating relevant knowledge from noisy and redundant input such as webpages . |
| Approach: | They propose to restructure free text into local knowledge graphs that are linearized into sequences . they propose to encode the graph as a sequence and then linearize it into a structured sequence . |
| Outcome: | The proposed method compresses web search information and reduces redundancy . it can be used for open-domain question answering and multi-document summarization tasks . |
Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation (D19-1)
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| Challenge: | Existing domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual sample samples. |
| Approach: | They propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from a source domain model. |
| Outcome: | The proposed model outperforms baselines and state-of-the-art models on three sequence labeling tasks. |
Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
| Outcome: | The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations. |
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)
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| Challenge: | Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
| Approach: | They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples. |
| Outcome: | The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
Distributionally Robust Language Modeling (D19-1)
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| Challenge: | Language models are generally trained on data spanning a wide range of topics but might be applied to an unknown target distribution. |
| Approach: | They propose a distributionally robust optimization procedure which minimizes the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. |
| Outcome: | The proposed method reduces the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. |
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling (D19-1)
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| Challenge: | Contextualized word embeddings are becoming a ubiquitous component of natural language processing. |
| Approach: | They propose a domain-adaptive fine-tuning approach to pretrain on unlabeled text . they test this approach on sequence labeling in two challenging domains . |
| Outcome: | The proposed approach improves on sequence labeling in two domains: Early Modern English and Twitter. |
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds (D19-1)
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| Challenge: | Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. |
| Approach: | They propose to use IRT models generated from artificial crowds of DNNs to learn IRT. |
| Outcome: | The proposed model learning method outperforms baseline methods for two NLP tasks. |
Parallel Iterative Edit Models for Local Sequence Transduction (D19-1)
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| Challenge: | Recent approaches to local sequence transduction are based on the popular encoder-decoder model for sequence to sequence learning. |
| Approach: | They propose a parallel iterative edit model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). |
| Outcome: | The proposed model is faster and more accurate than the current encoder-decoder model for local sequence transduction tasks like translation and paraphrasing. |
ARAML: A Stable Adversarial Training Framework for Text Generation (D19-1)
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| Challenge: | Existing generative adversarial networks suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. |
| Approach: | They propose a framework where the discriminator assigns rewards to samples acquired from a stationary distribution near the data rather than the generator’s distribution. |
| Outcome: | The proposed framework outperforms state-of-the-art text GANs with a more stable training process. |
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow (D19-1)
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| Challenge: | Neural sequence-to-sequence models are autoregressive, meaning they factor the joint probability of the output sequence into the product of probabilities over the next to-ken. |
| Approach: | They propose a non-autoregressive sequence generation model using latent variables . they use generative flow to model complex distributions using neural networks . |
| Outcome: | The proposed model performs comparable to state-of-the-art models and has constant decoding time w.r.t the sequence length. |
Compositional Generalization for Primitive Substitutions (D19-1)
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| Challenge: | Existing approaches to encoding compositional generalization are lacking . et al., 2017) argue that neural networks lack compositional ability . |
| Approach: | They propose a method to encode compositionality in neural networks using two representations . they reduce the entropy in each representation to improve generalization . |
| Outcome: | The proposed approach improves performance on five NLP tasks including instruction learning and machine translation. |
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (D19-1)
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| Challenge: | Large-scale training sets for pronoun resolution are scarce, since manually labelling data is costly. |
| Approach: | They propose a language-model-based approach to solve pronoun disambiguation problems using a WikiCREM dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on 6 out of 7 datasets. |
Identifying and Explaining Discriminative Attributes (D19-1)
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| Challenge: | Existing word vector representation models lack latent features (dense vectors) identifying discriminative attributes can motivate the development of word vector models with finer semantics. |
| Approach: | They propose to use a word vector representation model to identify discriminative attributes by combining knowledge graphs with images to construct explicit vector spaces. |
| Outcome: | The proposed model performs comparable to state-of-the-art systems while providing full model transparency and explainability. |
Patient Knowledge Distillation for BERT Model Compression (D19-1)
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| Challenge: | Pre-trained language models such as BERT have proven to be highly effective for natural language processing tasks, but the high demand for computing resources hinders their application in practice. |
| Approach: | They propose to compress an original large model (teacher) into an equally-effective lightweight shallow network (student) Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy. |
| Outcome: | The proposed model reduces the computational cost of training models using the teacher model into a lightweight shallow network. |
Neural Gaussian Copula for Variational Autoencoder (D19-1)
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| Challenge: | Variational language models assume the posterior of latent variables to be factorized even when the true posterior is not. |
| Approach: | They propose a Gaussian Copula Variational Autoencoder to avert a typical training problem called posterior collapse observed in all other variational language models. |
| Outcome: | The proposed model achieves great success over a huge number of tasks, such as transfer learning, unsupervised learning and unsupervised training. |
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (D19-1)
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| Challenge: | Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. |
| Approach: | They propose a new formulation of attention via the lens of the kernel which allows us to understand individual components of Transformer's attention. |
| Outcome: | The proposed model outperforms existing models on language understanding and sequence prediction tasks and is more efficient than existing models. |
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification (D19-1)
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| Challenge: | Existing models for multi-label classification ignore complexity and dependencies among labels . Experimental results show that our method can obtain more accurate multi-lab classification results. |
| Approach: | They propose a meta-learning method to capture complex label dependencies . they use a Meta-learner to jointly learn the training policies and prediction policies for different labels. |
| Outcome: | The proposed method can capture complex label dependencies on fine-grained entity typing and text classification tasks. |
Revealing the Dark Secrets of BERT (D19-1)
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| Challenge: | Existing models of BERT-based learning systems are lacking specific mechanisms that contribute to its success. |
| Approach: | They propose to use GLUE tasks to analyze the interpretation of self-attention, which is one of the underlying components of BERT. |
| Outcome: | The proposed model outperforms the regular model on GLUE tasks by disabling attention in certain heads. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Countering Language Drift via Visual Grounding (D19-1)
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| Challenge: | Emergent multi-agent communication protocols are different from natural language . a long-standing goal of artificial intelligence research is to develop agents that can cooperate with other agents . |
| Approach: | They propose to use syntactic and semantic constraints to improve communication . they propose to combine these constraints with auxiliary training constraints to reduce language drift . |
| Outcome: | a new study shows that pre-trained agents retain English syntax while learning to convey intended meaning . the proposed training constraints can be used to mitigate language drift . |
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives (D19-1)
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| Challenge: | a recent study has shown that deep neural networks are effective with various tasks . a new study examines how representations of tokens evolve between layers under different learning objectives . |
| Approach: | They use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers. |
| Outcome: | The proposed model outperforms untrained models on word identity prediction tasks . the model outpersforms models trained on other linguistic tasks based on the model's objective . |
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? (D19-1)
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| Challenge: | a series of bilingual lexicon induction experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods fail for a large number of language pairs. |
| Approach: | They propose to use fully unsupervised approaches to project monolingual embeddings into a shared cross-lingual space without any cross-linguistic signal. |
| Outcome: | The proposed methods fail for a large number of language pairs, but never surpass weakly supervised methods. |
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings (D19-1)
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| Challenge: | Current unsupervised adversarial methods for mapping monolingual word embeddings require high quality parallel data, such as a dictionary or sentence-aligned corpus. |
| Approach: | They propose a weakly-supervised adversarial training method which improves performance of previous unsupervised adversaries for most languages. |
| Outcome: | The proposed method improves the performance of previous unsupervised adversarial methods for most languages and especially for typologically distant language pairs. |
Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)
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| Challenge: | Existing knowledge graphs that represent entities in different languages are not covered by existing systems. |
| Approach: | They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other. |
| Outcome: | The proposed method significantly outperforms existing systems on two benchmark datasets. |
Contrastive Language Adaptation for Cross-Lingual Stance Detection (D19-1)
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| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
| Approach: | They propose a novel approach which leverages labeled data in one language to identify relative perspective of a document with respect to a claim in a different target language. |
| Outcome: | The proposed approach can deal with the challenge of limited labeled data in the target language. |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
Social IQa: Commonsense Reasoning about Social Interactions (D19-1)
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| Challenge: | Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations. |
| Approach: | They propose a crowdsourcing framework that collects commonsense questions along with correct and incorrect answers about social interactions. |
| Outcome: | The proposed framework mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. |
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning (D19-1)
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| Challenge: | Existing models for multi-hop question answering require multiple pieces of evidence scattered in a given context. |
| Approach: | They propose an interpretable, controller-based self-assembling Neural Modular Network for multi-hop reasoning . their model can softly decompose a multi-step question into multiple single-hop sub-questions . |
| Outcome: | The proposed model improves on the static, single-hop model on regular and adversarial evaluations. |
Posing Fair Generalization Tasks for Natural Language Inference (D19-1)
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| Challenge: | Existing evaluation methods for deep learning semantics rely on naturalistic corpora, but they often fail to support the kind of generalization we are asking for. |
| Approach: | They define and motivate a formal notion of fairness for evaluations of deep learning models for semantics . they then apply it to natural language inference by constructing challenging but provably fair artificial datasets based on the results . |
| Outcome: | The proposed evaluations show that standard neural models fail to generalize in the required ways and even these models do not solve the task perfectly. |
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text (D19-1)
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| Challenge: | XPAD is a new model that predicts actions' effects and their dependencies based on background knowledge . previous work on extracting sequences of actions from text has focused on identifying why they are the way they are . |
| Approach: | They propose a new model that biases effect predictions towards those that explain more of the actions in the paragraph and are more plausible with respect to background knowledge. |
| Outcome: | The proposed model outperforms existing systems on explaining actions by predicting dependencies while maintaining the performance on the original task in ProPara. |
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (D19-1)
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| Challenge: | Existing datasets for reading comprehension tasks have been used to test the generalization of natural language understanding systems. |
| Approach: | They propose a diagnostic benchmark suite to clarify key issues related to the robustness and systematicity of NLU systems. |
| Outcome: | The proposed benchmark suite clarifies key issues related to the robustness and systematicity of NLU systems. |
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset (D19-1)
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Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Ben Goodrich, Daniel Duckworth, Semih Yavuz, Amit Dubey, Kyu-Young Kim, Andy Cedilnik
| Challenge: | a lack of high quality conversational data is limiting progress in dialog systems . we present a dataset of 13,215 task-based dialogs . |
| Approach: | They propose a task-based dialog dataset which includes 13,215 task-related dialogs . they use a two-person, spoken "Wizard of Oz" approach and a "self-dialog" approach . |
| Outcome: | The taskmaster-1 dataset contains 13,215 task-based dialogs comprising six domains. |
Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data (D19-1)
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| Challenge: | a large number of goal-oriented dialogue datasets are limited in their size, linguistic diversity, domain coverage, or annotation granularity. |
| Approach: | They propose a multi-domain goal-oriented dialogue dataset that uses a crowd-sourced worker and a trained annotator to curate and annotate large scale data. |
| Outcome: | The proposed dataset is 8 times the size of the largest comparable dialogue dataset available to the public. |
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack (D19-1)
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| Challenge: | Detecting offensive language in the context of a dialogue is an increasingly important application of natural language processing. |
| Approach: | They propose to train a model to be robust to such attacks by iterative build it, break it, fix it scheme with humans and models in the loop. |
| Outcome: | The proposed model is significantly more robust to such human attacks than previous systems. |
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue (D19-1)
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| Challenge: | Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. |
| Approach: | They propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model . the model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . |
| Outcome: | The proposed model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . intrinsic evaluations on the resolution of ellipsis and co-reference show that the model outperforms the baseline model in terms of EM, BLEU and F1 . |
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
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| Challenge: | Existing memory networks do not perform well when leveraging heterogeneous information from different sources. |
| Approach: | They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model. |
| Outcome: | The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets. |
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (D19-1)
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| Challenge: | Existing aspects-based sentiment classification models lack a mechanism to account for relevant syntactical constraints and word dependencies. |
| Approach: | They propose to build a Graph Convolutional Network over the dependency tree of a sentence to exploit syntactical information and word dependencies. |
| Outcome: | The proposed model is comparable to state-of-the-art models on three benchmarking collections. |
Coupling Global and Local Context for Unsupervised Aspect Extraction (D19-1)
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| Challenge: | Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data. |
| Approach: | They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts. |
| Outcome: | The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks. |
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)
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| Challenge: | Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data. |
| Approach: | They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space . |
| Outcome: | The proposed method can learn weights for words to achieve fine-grained adaptation. |
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (D19-1)
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| Challenge: | Existing methods for aspect-specific sentiment classification are noisy and downgraded performance. |
| Approach: | They propose a constrained attention network to regularize attention for multi-aspect sentiment analysis by orthogonal regularization on multiple aspects and sparse regularization for each single aspect. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets and extends to multi-task settings. |
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training (D19-1)
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| Challenge: | Current weakly supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. |
| Approach: | They propose a weakly supervised approach that leverages seed words for aspect detection . they propose supervised student-teacher approach that uses teacher to train student models . |
| Outcome: | The proposed approach outperforms previous weakly supervised approaches by 14.1 F1 points on average in six domains of product reviews and six multilingual datasets of restaurant reviews. |
Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (D19-1)
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| Challenge: | Existing studies on text-image content have focused on image as primary content, and text as secondary content. |
| Approach: | They propose a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies . they show that employing both text and image improves intent detection by 9.6 . |
| Outcome: | The proposed model shows that using both text and image improves intent detection by 9.6 compared to using only the image modality. |
Neural Conversation Recommendation with Online Interaction Modeling (D19-1)
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| Challenge: | Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions. |
| Approach: | They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
Text-based inference of moral sentiment change (D19-1)
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| Challenge: | Existing work in NLP treats moral sentiment as a flat classification problem, but our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and finegrained categories informed by Moral Foundations Theory. |
| Approach: | They propose a text-based framework that exploits implicit moral biases learned from diachronic word embeddings to probe moral sentiment change over a long historical period. |
| Outcome: | The proposed framework supports inferences of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions. |
Detecting Causal Language Use in Science Findings (D19-1)
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| Challenge: | Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. |
| Approach: | They developed a prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal” and “direct causal” categories. |
| Outcome: | The proposed model can be used to identify the inappropriate use of causal language in scientific publications and news articles. |
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)
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Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
| Challenge: | Existing efforts to verify factual claims are limited by small datasets or artificially constructed datasets. |
| Approach: | They propose to use the largest publicly available dataset of naturally occurring factual claims for automatic claim verification. |
| Outcome: | The proposed model outperforms baseline models and evidence pages significantly. |
A Deep Neural Information Fusion Architecture for Textual Network Embeddings (D19-1)
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| Challenge: | Textual network embeddings aim to learn a low-dimensional representation for every node in the network while seeking to retain the original network information. |
| Approach: | They propose a deep neural architecture to fuse the two kinds of informations into one representation. |
| Outcome: | The proposed model outperforms the comparing methods on all three datasets. |
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)
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| Challenge: | Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations. |
| Approach: | They propose a model that captures the sociological phenomenon of homophily and combines it with linguistic information to make a prediction. |
| Outcome: | The proposed model significantly outperforms existing models on three different tasks and is compared with other models. |
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis (D19-1)
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| Challenge: | a new study examines the use of labeled and unlabeled corpora in political science research . large corporata often contain documents of a certain subject or type, but they are often unlabed . a recent study found that labeles with pertinent documents stem from a single source . |
| Approach: | They propose an unsupervised domain adaptation framework that uses a text classification model and time-aware training to ensure it works well with diachronic corpora. |
| Outcome: | The proposed framework outperforms benchmarks on an expert-annotated dataset and is more stable and learns better representations. |
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation (D19-1)
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| Challenge: | Existing methods to estimate user location ignore hierarchical structure among locations. |
| Approach: | They propose a hierarchical location prediction neural network for Twitter user geolocation that first predicts the home country for a user, then uses the country result to guide the city-level prediction. |
| Outcome: | The proposed model can achieve state-of-the-art results over three common benchmarks under different feature settings and greatly reduces the mean error distance. |
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop (D19-1)
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| Challenge: | Recent efforts focused on detecting antisocial behavior after the fact . a forecasting model needs to capture the flow of the conversation, not individual comments . real conversations have an unknown horizon; therefore a practical forecasting system needs to assess the risk . |
| Approach: | They propose a conversational forecasting model that learns conversational dynamics and exploits it to predict derailment as the conversation develops. |
| Outcome: | The proposed model outperforms state-of-the-art models at forecasting derailment . it learns an unsupervised representation of conversational dynamics and exploits it to predict future derailments . |
A Benchmark Dataset for Learning to Intervene in Online Hate Speech (D19-1)
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| Challenge: | Existing methods to detect online hate speech ignore conversational context . generative hate speech intervention is a novel approach to counter online hate . |
| Approach: | They propose a task where generative hate speech intervention generates responses to intervene during online conversations that contain hate speech. |
| Outcome: | The proposed method can detect and block hate speech and discourage it . it can also generate responses written by Mechanical Turk workers . |
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
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| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums (D19-1)
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| Challenge: | a dataset of written code-switched productions is curated from topical threads of multiple bilingual communities on the Reddit discussion platform. |
| Approach: | They analyze a dataset of written code-switched productions curated from multiple bilingual communities on the reddit discussion platform and examine whether findings are carried over to written codeswitching in discussion forums. |
| Outcome: | The proposed dataset can facilitate a range of research and practical activities. |
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity (D19-1)
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| Challenge: | Existing methods to verify rumors are needed to identify false rumors. |
| Approach: | They propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter that exploits the temporal dynamics of stance evolution. |
| Outcome: | The proposed framework outperforms previous methods on two benchmark datasets showing that it can predict rumor stance and veracity. |
Reconstructing Capsule Networks for Zero-shot Intent Classification (D19-1)
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| Challenge: | Existing methods for intent classification are limited due to fast-emerging intents . a recent study shows that existing methods are not effective in recognizing unseen intents. |
| Approach: | They propose to reconstruct capsule networks for zero-shot intent classification by using latent information from labeled utterances. |
| Outcome: | The proposed method outperforms existing methods on two task-oriented dialogue datasets in different languages. |
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network (D19-1)
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| Challenge: | Existing methods to solve person-job fit in single-domain setting are limited by labeled data. |
| Approach: | They propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. |
| Outcome: | The proposed model is effective when there is not enough labeled data. |
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (D19-1)
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| Challenge: | Existing studies on short text classification focus on long texts and achieve unsatisfactory performance due to the sparsity and limited labeled data. |
| Approach: | They propose a heterogeneous graph neural network based method for semi-supervised short text classification that leverages the full advantage of few labeled data and large unlabeled data through information propagation along the graph. |
| Outcome: | The proposed method outperforms state-of-the-art methods across six benchmark datasets significantly. |
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts (D19-1)
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Elissa Redmiles, Lisa Maszkiewicz, Emily Hwang, Dhruv Kuchhal, Everest Liu, Miraida Morales, Denis Peskov, Sudha Rao, Rock Stevens, Kristina Gligorić, Sean Kross, Michelle Mazurek, Hal Daumé III
| Challenge: | Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. |
| Approach: | They present a comparison of the validity of well-known readability measures and introduce a novel approach to measure readability at scale. |
| Outcome: | The proposed approach addresses shortcomings of existing measures. |
News2vec: News Network Embedding with Subnode Information (D19-1)
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| Challenge: | Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news. |
| Approach: | They propose a model that integrates news features and inter-textual knowledge into a dense vector representation. |
| Outcome: | The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks . |
Recursive Context-Aware Lexical Simplification (D19-1)
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| Challenge: | REC-LS is a system that can be used to perform a number of simplifications at once, but the results are sometimes ungrammatical and meaning can be changed, making the original text less clear and more complex. |
| Approach: | They propose a recursive context-aware lexical simplification architecture that takes previous simplification steps into account and makes use of the wider context when detecting the words in need of simplification. |
| Outcome: | The proposed system outperforms the current state-of-the-art systems in lexical simplification. |
Leveraging Medical Literature for Section Prediction in Electronic Health Records (D19-1)
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| Challenge: | Prior approaches to section prediction have only used text data from EHRs and required significant manual annotation. |
| Approach: | They propose to use sections from medical literature to train models to predict sections in EHRs. |
| Outcome: | The proposed model uses sections from medical literature that contain similar content to those found in EHR sections. |
Neural News Recommendation with Heterogeneous User Behavior (D19-1)
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| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)
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| Challenge: | Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user. |
| Approach: | They propose to use review content and user-item graphs to integrate them as different views. |
| Outcome: | The proposed approach can learn user and item representations from review content and user-item graphs. |
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification (D19-1)
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| Challenge: | Existing studies on adversarial attacks on deep learning models focus on generation of adversarials and defense against adversarial attacks. |
| Approach: | They propose a framework to identify and adjust malicious perturbations and block adversarial attacks for machine learning models. |
| Outcome: | The proposed framework outperforms baseline methods in blocking adversarial attacks for text classification models. |
A Neural Citation Count Prediction Model based on Peer Review Text (D19-1)
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| Challenge: | Recent studies have focused on extracting or mining useful features from the paper itself or the associated authors. |
| Approach: | They propose to utilize peer review data for the CCP task with a neural prediction model to learn a comprehensive semantic representation for peer review text. |
| Outcome: | The proposed model improves on the peer review data and hand-crafted features. |
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (D19-1)
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| Challenge: | Existing approaches to document-level relation extraction use nodes and edges as relations between nodes. |
| Approach: | They propose an edge-oriented graph neural model for document-level relation extraction that uses different types of nodes and edges to create a document-based graph. |
| Outcome: | The proposed model can learn intra- and inter-sentence relations using multi-instance learning internally. |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)
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| Challenge: | Text style transfer task has long suffered from the shortage of parallel data . |
| Approach: | They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it. |
| Outcome: | The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning. |
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives (D19-1)
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| Challenge: | Privacy policies are long and complex documents that are difficult for users to read and understand. |
| Approach: | They present a corpus of 1750 questions about privacy policies of mobile applications and over 3500 expert annotations of relevant answers. |
| Outcome: | The proposed corpus of 1750 questions on privacy policies shows that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA. |
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation (D19-1)
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Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
| Challenge: | Existing models for product description generation do not take the product attribute information into account. |
| Approach: | They propose a model that takes the embedding and the entity label of each word into account . they establish a keyword memory that stores the entity labels as keys and keywords as values . |
| Outcome: | The proposed model increases the fidelity of the generated descriptions by 25%. |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (D19-1)
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| Challenge: | Existing methods for inferring the fine-grained type of an entity from knowledge base are incomplete and lack type information. |
| Approach: | They propose a novel Deep Learning architecture to infer the fine-grained type of an entity from a knowledge base. |
| Outcome: | The proposed method significantly outperforms four state-of-the-art methods on two large-scale datasets. |
Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
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| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm (D19-1)
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| Challenge: | spectral topic models lack reliability in real data and lack of practical implementations. |
| Approach: | They propose to use a spectral topic inference method to infer correlations between topics in real data and a matrix-based approach to inference. |
| Outcome: | The proposed method outperforms tensor-based methods and probabilistic methods in real data and provides a complete guide to correlated topic modeling. |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
PRADO: Projection Attention Networks for Document Classification On-Device (D19-1)
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| Challenge: | Recent advances in deep learning have improved the performance of on-device neural networks for long text classification. |
| Approach: | They propose a projection attention neural network PRADO that combines trainable projections with attention and convolutions to train tiny neural networks that achieve high performance on multiple long document classification tasks. |
| Outcome: | The proposed model achieves high performance on multiple long document classification tasks while maintaining compact size. |
Subword Language Model for Query Auto-Completion (D19-1)
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| Challenge: | Current neural query auto-completion systems rely on character-level language models but they slow down when queries are long. |
| Approach: | They propose to use subword language models for query auto-completion . they propose to implement a retrace algorithm and a method for approximate marginalization . |
| Outcome: | The proposed model achieves 2.5 times faster while maintaining similar quality to character-level baseline. |
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
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| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
Counterfactual Story Reasoning and Generation (D19-1)
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| Challenge: | a desired property of AI systems is counterfactual reasoning: ability to predict causal changes in future events. |
| Approach: | They propose to rewrite a short story and a counterfactual event to make it compatible with the given counterfact. |
| Outcome: | The proposed task requires deep understanding of causal narrative chains and counterfactual invariance . the proposed dataset includes 81,407 counterfact "branches" without a rewritten storyline . |
Encode, Tag, Realize: High-Precision Text Editing (D19-1)
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| Challenge: | Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts. |
| Approach: | They propose a sequence tagging approach that casts text generation as a text editing task. |
| Outcome: | The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance. |
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)
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| Challenge: | Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions. |
| Approach: | They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation. |
| Outcome: | The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions. |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)
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| Challenge: | Existing methods for news comment generation have not been well studied. |
| Approach: | They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network. |
| Outcome: | The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets. |
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features (D19-1)
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| Challenge: | Existing methods for text generation are limited in supervised setting and designed for specific applications. |
| Approach: | They propose a text generation model that learns semantics and structural features simultaneously . their model leverages a topic-based model to enhance the recognition of text semantics . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of text perplexity and topic coherence. |
LXMERT: Learning Cross-Modality Encoder Representations from Transformers (D19-1)
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| Challenge: | Existing models with better representations of visual content and language have been developed for visual-content understanding. |
| Approach: | They propose a framework to learn vision-and-language connections from Transformers models . they pre-train a large-scale Transformer model with large amounts of image-and sentence pairs . |
| Outcome: | The proposed model improves state-of-the-art on two visual-reasoning tasks by 22% . the proposed model is based on a large-scale Transformer model with three encoders . |
Phrase Grounding by Soft-Label Chain Conditional Random Field (D19-1)
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| Challenge: | Existing methods to ground entities depend on inference or non-differentiable losses. |
| Approach: | They propose a phrase grounding task that grounds entities to corresponding regions in an image . they use neural chain Conditional Random Fields to model dependencies among regions . |
| Outcome: | The proposed method is based on a dataset of the Flickr30k Entities dataset. |
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues (D19-1)
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| Challenge: | a core task of natural language understanding is to ground a pronoun to a visual object it refers to . problem arises when people use pronounos to refer to something they can see without prior introduction . a novel visual-aware PCR model is proposed to solve this problem . |
| Approach: | They propose a visual-aware PCR model to ground a pronoun to a visible object . they propose PCR using a large-scale dialogue dataset to investigate this problem . |
| Outcome: | The proposed model can help resolve pronouns in conversational contexts. |
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension (D19-1)
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| Challenge: | Multimodal semantic comprehension has attracted increasing research interest recently such as visual question answering and caption generation. |
| Approach: | They propose to use a large-scale multimodal instructional video dataset to support fine-grained comprehension research in specific domain. |
| Outcome: | The proposed dataset contains 2,800 videos from YouTube, spanning more than 420 hours in total. |
DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization (D19-1)
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| Challenge: | Existing models for natural language video localization are top-down and bottom-up . however, both approaches suffer several limitations, leading to performance degradation . |
| Approach: | They propose a top-down approach for localizing a natural language description in a video sequence . they propose 'DEnse Bottom-Up Grounding' which uses the temporal boundaries of each video frame . |
| Outcome: | The proposed framework matches the speed of top-down models while surpassing the state-of-the-art models. |
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (D19-1)
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| Challenge: | Named entity recognition (NER) models can identify labels in 5.38% of test sentences . a framework to handle label mistakes during NER model training is proposed . |
| Approach: | They propose a framework to manually correct label mistakes in named entity recognition (NER) they aim to improve the accuracy of models by re-evaluating popular models on corrected test sets . |
| Outcome: | The proposed framework can detect label mistakes in 5.38% of test sentences . the proposed framework improves on three datasets with a high-performance model . |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
Open Domain Web Keyphrase Extraction Beyond Language Modeling (D19-1)
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| Challenge: | Recent neural methods for keyphrase extraction are mostly observed in documents originating from the scientific domain. |
| Approach: | They develop a neural keyphrase extraction model that goes beyond language understanding to handle the variations of domain and content quality. |
| Outcome: | The proposed model can handle the variations of domain and content quality without restriction of the domain, quality, nor content of the documents. |
TuckER: Tensor Factorization for Knowledge Graph Completion (D19-1)
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| Challenge: | Knowledge graphs contain only a small subset of all possible facts . link prediction is a task of inferring missing facts based on existing facts - knowledge graphs are expensive and lack of information is needed to add new information. |
| Approach: | They propose a linear model based on Tucker decomposition of knowledge graph triples . they show that the model is expressive and has sufficient bounds on its embedding dimensionalities . |
| Outcome: | The proposed model outperforms state-of-the-art models across standard datasets and acts as a strong baseline for more elaborate models. |
Human-grounded Evaluations of Explanation Methods for Text Classification (D19-1)
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| Challenge: | Explainable Artificial Intelligence (XAI) is aimed at providing explanations for decisions made by AI systems. |
| Approach: | They propose to use model-agnostic and model-specific explanation methods for CNNs for text classification to provide human-grounded evaluations. |
| Outcome: | The proposed methods could be used to explain models' results and improve AIs and humans in many cases. |
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)
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| Challenge: | Existing academic search engines cannot detect relevant papers where a resource is mentioned. |
| Approach: | They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework . |
| Outcome: | The proposed model achieves the best results on both the classification task and recommendation task. |
Adversarial Reprogramming of Text Classification Neural Networks (D19-1)
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| Challenge: | Recent studies have shown that adversarial examples can cause a machine learning model to misclassify a sample from the classifier's input domain. |
| Approach: | They propose a context-based vocabulary remapping method that performs a computationally inexpensive input transformation to reprogram a victim classification model for a new set of sequences. |
| Outcome: | The proposed method performs a cost-effective input transformation to reprogram a model for a new set of sequences without altering the network architecture or parameters. |
Document Hashing with Mixture-Prior Generative Models (D19-1)
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| Challenge: | Existing generative hashing methods only consider the use of simple priors, which limits them to further improve their performance. |
| Approach: | They propose to use Gaussian and Bernoulli priors to generate hashing codes . they propose to cast a Gausssian latent representation into binary code . |
| Outcome: | The proposed models outperform existing methods on a benchmark dataset using Gaussian and Bernoulli priors. |
On Efficient Retrieval of Top Similarity Vectors (D19-1)
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| Challenge: | Existing representation learning methods such as Word2vec represent word embeddings in the semantic space. |
| Approach: | They propose an efficient method for searching vectors via a non-metric matching function: inner product. |
| Outcome: | Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods. |
Multiplex Word Embeddings for Selectional Preference Acquisition (D19-1)
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| Challenge: | Existing word embeddings are limited in their ability to represent fixed vectors . instead, they incorporate relational dependencies of different words into their embeddables - a limitation that is addressed by a multiplex model . |
| Approach: | They propose a word embedding model which incorporates relational dependencies of different words into their embeddables. |
| Outcome: | The proposed model can be easily extended according to various relations among words. |
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model (D19-1)
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| Challenge: | a large number of parameters dominate the memory usage of deep neural nets . compression of embedding layers is the key to reducing memory usage . |
| Approach: | They propose a multi-way multiplicative neural compressor to compress embedding layers . they use an adaptively created matrix and multiplicativative compositions to learn them . |
| Outcome: | a new multi-way multiplicative neural compressor can achieve 41.38 times compression rate with little loss in performance. |
It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution (D19-1)
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| Challenge: | Existing attempts to mitigate gender bias rely on operationalisation of gender bias as a projection over a linear subspace. |
| Approach: | They propose to operationalise gender bias as a linear subspace and augmented a corpus to remove bias by swapping all inherently-gendered words in the copy. |
| Outcome: | The proposed approach outperforms projection-based methods at the task of drawing non-biased gender analogies by an average of 19% across both corpora. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation (D19-1)
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| Challenge: | Existing work characterizes differences in meaning between words across languages using semantic relations . however, because of translation ambiguity, semantic relations are not always preserved by translation. |
| Approach: | They propose a cross-lingual relation classifier trained only with English examples and a bilingual dictionary to account for translation ambiguity when transferring knowledge from English to cross-linguistic settings. |
| Outcome: | The proposed model outperforms baselines that rely on bilingual embeddings or dictionaries for cross-lingual transfer and approaches fully supervised systems on English tasks. |
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
Do NLP Models Know Numbers? Probing Numeracy in Embeddings (D19-1)
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| Challenge: | Existing models cannot capture numeracy, but they can be useful for complex reasoning tasks. |
| Approach: | They investigate numerical reasoning capabilities of a question-answering model . they probe token embedding methods on synthetic list maximum, number decoding, and addition tasks. |
| Outcome: | The proposed model excels on questions that require numerical reasoning, i.e., it already captures numeracy. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
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| Challenge: | Context-dependent semantic parsing has proved to be an important but challenging task. |
| Approach: | They propose to perform follow-up query analysis to restate context-dependent queries with contextual information. |
| Outcome: | The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising . |
Text2Math: End-to-end Parsing Text into Math Expressions (D19-1)
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| Challenge: | Empirical results on benchmark datasets demonstrate the efficacy of our approach. |
| Approach: | They propose a model for semantically parsing text into math expressions and propose 'text2math' which aims to predict the complete math expression as a tree structure, with minimal manual efforts. |
| Outcome: | Empirical results on benchmark datasets demonstrate the efficacy of the proposed model. |
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)
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Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Generating SQL queries from user utterances is an important task to help end users acquire information from databases. |
| Approach: | They propose a context-dependent text-to-SQL generation task that edits previous queries . they use an utterance-table encoder and a table-aware decoder to incorporate context . |
| Outcome: | The proposed model is flexible to change individual tokens and robust to error propagation. |
Syntax-aware Multilingual Semantic Role Labeling (D19-1)
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| Challenge: | Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation. |
| Approach: | They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. |
Cloze-driven Pretraining of Self-attention Networks (D19-1)
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| Challenge: | Existing work on pretraining language models has used unidirectional (left-to-right) or bi-directional (both left-to right and right-to left) LMs with loss function. |
| Approach: | They propose a bi-directional transformer model that pretrains both directions of a large language-model-inspired self-attention cloze model and propose clozing to predict each word in the training data. |
| Outcome: | The proposed model performs well on GLUE and state of the art benchmarks consistent with BERT. |
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)
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| Challenge: | Existing techniques for relevance and semantic matching cannot be easily adapted to the other. |
| Approach: | They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness. |
| Outcome: | The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness. |
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling (D19-1)
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| Challenge: | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. |
| Approach: | They propose to use a unified span-based model for Chinese SRL as a strong baseline. |
| Outcome: | The proposed framework achieves state-of-the-art 87.54 and 88.5 F1 scores on the Chinese Proposition Bank and CoNLL-2009 datasets. |
Transfer Fine-Tuning: A BERT Case Study (D19-1)
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| Challenge: | Recent advances in sentence representation learning have improved the performance of BERT models, but the computational power required is an obstacle preventing practical applications from adopting the technology. |
| Approach: | They propose to inject phrasal paraphrase relations into BERT to generate suitable representations for semantic equivalence assessment instead of increasing model size. |
| Outcome: | The proposed model improves a smaller model while maintaining the model size. |
Data-Anonymous Encoding for Text-to-SQL Generation (D19-1)
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| Challenge: | Existing approaches to handle table-related tokens before the semantic parser are not efficient . existing approaches ignore handling table- related tokens or use deterministic approaches based on string-match or word embedding similarity. |
| Approach: | They propose a more efficient approach to handle table-related tokens before the parser . they propose tagging a sequential tabbing problem and an implicit supervision approach . |
| Outcome: | The proposed approach significantly outperforms deterministic approaches. |
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks (D19-1)
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| Challenge: | State-of-the-art SRL models do not model non-local interaction between arguments . e.g., LSTMs do not allow for efficient inference . |
| Approach: | They propose a new approach to model interactions between arguments using capsule networks . they analyze errors in the refinement procedure by capturing intuition in a flexible way . |
| Outcome: | The proposed model outperforms the baseline model on all 7 languages and achieves state-of-the-art results on 5 languages including English. |
Learning Programmatic Idioms for Scalable Semantic Parsing (D19-1)
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| Challenge: | In state-of-the-art semantic parsers map natural language instructions to source code . idioms improve the accuracy of semantic parses, allowing for faster decoding . |
| Approach: | They propose an iterative method to extract code idioms from large source code corpora . they use most-frequent subtrees of their syntax trees to train semantic parsers to apply them . |
| Outcome: | The proposed method improves the state-of-the-art semantic parsers' accuracy and training time by more than 50%. |
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation (D19-1)
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| Challenge: | Interactive programming with interleaved code snippet cells and natural language markdown is gaining popularity in the form of Jupyter notebooks. |
| Approach: | They propose to train code generation models based on a corpus of 1.5 million examples with a curated test set of 3.7K instances based off online programming assignments. |
| Outcome: | The proposed model generates code cells based on the NL-Code history and human-curated data. |
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study (D19-1)
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| Challenge: | Existing semantic parsing technologies are not well-suited for use in real-world applications. |
| Approach: | They propose a model-based intelligent agent that generates a clarification question in natural language . they propose 'interactive semantic parsing' with a human user in the loop . |
| Outcome: | The proposed approach improves both parsing accuracy and user confidence . it is demonstrated on two text-to-SQL datasets with different state-of-the-art parsers . |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (D19-1)
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| Challenge: | Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. |
| Approach: | They propose a target-dependent graph attention network for aspect level sentiment classification . it explicitly utilizes the dependency relationship among words to propagate sentiment features . they show that using BERT representations further substantially boosts the performance . |
| Outcome: | The proposed method outperforms baselines with GloVe embeddings and improves with BERT representations. |
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis (D19-1)
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| Challenge: | Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space. |
| Approach: | They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model. |
| Outcome: | The proposed model outperforms existing models by capturing implicit and explicit structural information. |
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)
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| Challenge: | Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features. |
| Approach: | They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context. |
| Outcome: | The proposed model achieves state-of-the-art on three datasets. |
Emotion Detection with Neural Personal Discrimination (D19-1)
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| Challenge: | Existing approaches to automatically predict the emotions of posts consider each post individually and predict their emotions independently. |
| Approach: | They propose a Neural Personal Discrimination approach to identify personal attributes from posts and connect relevant posts with similar attributes to jointly learn their emotions. |
| Outcome: | The proposed approach improves on existing models by capturing attributes-aware words and predicting emotions among relevant posts. |
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)
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| Challenge: | Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation . |
| Approach: | They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently . |
| Outcome: | The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets. |
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification (D19-1)
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| Challenge: | Existing text classification models are fragile and sensitive to simple perturbations. |
| Approach: | They propose a generator-classifier adversarial training approach to improve classification models . they use a large-scale lexical knowledge base to generate attacking examples . |
| Outcome: | The proposed approach outperforms strong baselines and reduces test errors on neural networks. |
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery (D19-1)
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| Challenge: | Existing approaches to inference opinionated text do not capture attributes in a one-off manner. |
| Approach: | They propose a probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. |
| Outcome: | The proposed model outperforms state-of-the-art models and yields intuitive topics. |
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining (D19-1)
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| Challenge: | Existing work on fine grained opinion annotations rely only on coarsely labeled opinions. |
| Approach: | They propose to use hierarchical structure of opinions to build a fine and coarse grained opinion model that exploits different views of the opinion expression. |
| Outcome: | The proposed model outperforms existing models on a recently released multimodal fine grained annotated corpus on IMDB and social networks. |
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
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| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (D19-1)
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| Challenge: | Existing approaches to cross-domain sentiment classification focus on domain-invariant representations, but few focus on the domain-specific information. |
| Approach: | They propose to distill domain-invariant sentiment features with an orthogonal domain-dependent task . the orthogonalist task is built on the aspects varying widely in different domains . |
| Outcome: | The proposed method improves domain-invariant features and transfer performance on three public datasets. |
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)
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| Challenge: | Existing models use aspect-independent encoders for sentence representation generation. |
| Approach: | They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture. |
| Outcome: | The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features. |
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)
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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
| Challenge: | Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. |
| Approach: | They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem. |
| Outcome: | The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines. |
A Dataset of General-Purpose Rebuttal (D19-1)
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument. |
| Approach: | They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text. |
| Outcome: | The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available . |
Rethinking Attribute Representation and Injection for Sentiment Classification (D19-1)
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| Challenge: | Existing models that use text attributes to improve sentiment classification use text as a categorical feature. |
| Approach: | They propose to represent attributes as chunk-wise importance weight matrices and consider four locations to inject attributes. |
| Outcome: | The proposed method outperforms the state-of-the-art and outperformed previous models. |
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)
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| Challenge: | Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information . |
| Approach: | They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge. |
| Outcome: | The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Fine-Grained Analysis of Propaganda in News Article (D19-1)
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| Challenge: | Existing methods for detecting propaganda are noisy and lack of explainability. |
| Approach: | They propose to perform fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. |
| Outcome: | The proposed model outperforms several strong BERT-based baselines. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
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| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |
Sequential Learning of Convolutional Features for Effective Text Classification (D19-1)
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| Challenge: | Existing models for text classification have largely ignored convolution filters and max pooling . text classification is one of the major applications of natural language processing . |
| Approach: | They propose a convolutional attentive recurrent network model which uses convolution filters and max pooling to improve text classification. |
| Outcome: | The proposed model outperforms existing convolutional models on text classification tasks. |
The Role of Pragmatic and Discourse Context in Determining Argument Impact (D19-1)
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| Challenge: | Recent work shows that attributes of both the audience and communicator constitute important cues for determining argument strength. |
| Approach: | They propose to use a dataset to study the pragmatic and discourse context of argumentative claims to build predictive models that incorporate the pragmatic context of the argument. |
| Outcome: | The proposed models outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
| Approach: | They propose to evaluate DA methods from two perspectives to determine their generalization ability . they find that DA method's test performance does not exhibit consistent improvements across translation tasks . |
| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
Simple and Effective Noisy Channel Modeling for Neural Machine Translation (D19-1)
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| Challenge: | Previous work on noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. |
| Approach: | They propose to use a standard sequence to sequence model which utilizes the entire source and target sentences to estimate posterior probability of a target sequence y given a source sequence x. |
| Outcome: | The proposed model outperforms direct models on German-English translations by up to 3.2 BLEU on four language pairs. |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (D19-1)
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| Challenge: | Pretrained language models require unlabelled data for training, while cross-lingual models underperform on low-resource languages. |
| Approach: | They propose a multi-lingual language model fine-tuning to train and fine- tune language models efficiently in their own language. |
| Outcome: | The proposed method outperforms existing models on two widely used datasets on cross-lingual classification tasks. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
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| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking. |
| Approach: | They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments. |
| Outcome: | The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models. |
Multilingual Grammar Induction with Continuous Language Identification (D19-1)
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| Challenge: | Existing methods to induce grammars of multiple languages do not consider language similarity measures. |
| Approach: | They propose a universal grammar induction approach that captures similarity between languages . they use vector representations to capture similarity and softly tie grammar parameters . |
| Outcome: | The proposed approach performs well over monolingual and multilingual datasets. |
Quantifying the Semantic Core of Gender Systems (D19-1)
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| Challenge: | a large number of languages employ grammatical gender on the lexeme, but is it truly arbitrary? a recent study shows that the relationship between grammamatical gender and lexical semantics is opaque. |
| Approach: | They propose a method to correlating inanimate nouns' gender with lexical semantics . they find that the gender systems of 18 languages exhibit a significant correlation with a definition . |
| Outcome: | a new study shows that the gender assignments of 18 languages are arbitrary . the authors show that the correlation between gender and semantics is significant . |
Perturbation Sensitivity Analysis to Detect Unintended Model Biases (D19-1)
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| Challenge: | Recent research shows that data-driven NLP models may inadvertently capture, reflect and sometimes amplify various social biases present in the language data they are trained on. |
| Approach: | They propose a generic evaluation framework that detects unintended model biases related to named entities and requires no new annotations or corpora. |
| Outcome: | The proposed framework detects unintended model biases related to named entities and requires no new annotations or corpora. |
Automatically Inferring Gender Associations from Language (D19-1)
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| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
| Outcome: | The proposed methods outperform strong baselines in large-scale studies of how people talk about women and men in two different settings. |
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes (D19-1)
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Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari, Brendan Kennedy, Gwenyth Portillo Wightman, Elaine Gonzalez, Natalie Delong, Rhea Bhatia, Arineh Mirinjian, Xiang Ren, Morteza Dehghani
| Challenge: | Existing estimates of hate crimes in the US are under-reported relative to actual number of incidents. |
| Approach: | They propose to use event extraction and multi-instance learning to predict hate crimes in local news articles for cities without official FBI reports. |
| Outcome: | The proposed model compares to FBI reports and shows that hate crimes are under-reported in local press. |
Minimally Supervised Learning of Affective Events Using Discourse Relations (D19-1)
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| Challenge: | Existing methods for learning affective events that trigger positive or negative sentiment are difficult because of the unbounded combinatorial nature of language. |
| Approach: | They propose to propagate affective polarity using discourse relations using a small seed lexicon and large raw corpus. |
| Outcome: | The proposed method learns affective events effectively without manually labeled data, and improves supervised learning when labeles are small. |
Event Detection with Multi-Order Graph Convolution and Aggregated Attention (D19-1)
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| Challenge: | Existing methods for event detection use first-order syntactic relations to identify trigger words. |
| Approach: | They propose a dependency tree-based method to model and aggregate multi-order syntactic representations in sentences. |
| Outcome: | The proposed method outperforms existing methods on a benchmark dataset . it uses a dependency tree based graph convolution network with aggregative attention . |
Coverage of Information Extraction from Sentences and Paragraphs (D19-1)
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| Challenge: | Textual information extraction (IE) uses textual features to negate stronger statements, such as the negation of stronger statements. |
| Approach: | They propose to use textual features to predict whether a given text segment mentions all objects standing in a certain relationship with a subject. |
| Outcome: | The proposed features can predict whether a given text segment mentions all objects standing in a certain relationship with a particular subject. |
HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)
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| Challenge: | Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles. |
| Approach: | They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles. |
| Outcome: | The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy. |
Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
| Outcome: | The proposed framework achieves state-of-the-art results on four datasets from a variety of domains. |
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains (D19-1)
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| Challenge: | Discourse relation classification is one of the most difficult tasks in discourse parsing. |
| Approach: | They propose a bidirectional encoder representation from transformer model that encodes a representation of likely next sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art system in 11-way classification by 8% points on the standard PDTB dataset. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |
BERT for Coreference Resolution: Baselines and Analysis (D19-1)
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| Challenge: | Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition. |
| Approach: | They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks. |
| Outcome: | A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing. |
Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs (D19-1)
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| Challenge: | a novel approach to paragraph planning involves a high-level control of different levels of relations between sentences . a proposed model with both forms of relations outperforms baselines in partially conditioned paragraph generation task . |
| Approach: | They propose two models that integrate human-created and latent relations into document-level language models . they focus on paragraph-level plan between sentences to produce coherent text . |
| Outcome: | The proposed models outperform baselines in partially conditioned paragraph generation task. |
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)
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| Challenge: | Existing methods for recognizing event causality written in web texts ignore each annotator's independent judgments, but we exploit each anorator''s judgments to predict the majority vote labels. |
| Approach: | They propose to grasp each annotator's policy by training multiple classifiers that predict the labels given by a single annotators and combine the outputs to predict the final labels determined by majority vote. |
| Outcome: | The proposed methods grasp each annotator's policy and combine the outputs to predict the final labels determined by majority vote. |
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons (D19-1)
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| Challenge: | Biological neural systems consist of a huge number of neurons, and can react to the environment in complicated ways. |
| Approach: | They propose a metric to quantify the sensitivity of neurons to each label and conduct experiments to prove it. |
| Outcome: | The proposed metric is based on a set of experiments that show that dropping an arbitrary neuron significantly degrades the accuracy of the model. |
Quantity doesn’t buy quality syntax with neural language models (D19-1)
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| Challenge: | Recurrent neural network language models can learn to predict upcoming words with remarkably low perplexity . but in syntactically complex contexts, they often assign unexpectedly high probabilities to ungrammatical words . |
| Approach: | They investigate whether recurrent neural networks can learn to predict upcoming words with remarkably low perplexity. |
| Outcome: | The proposed models perform worse than GPT and BERT in some constructions than LSTMs in other contexts. |
Higher-order Comparisons of Sentence Encoder Representations (D19-1)
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| Challenge: | a technique developed by neuroscientists compares activity patterns of different measurement modalities . a recent study examined the correspondence between popular pretrained language encoders and human processing difficulty . |
| Approach: | They employ a technique to compare activity patterns of different measurement modalities . they establish a correspondence between widely-employed pretrained language encoders and human processing difficulty . |
| Outcome: | The proposed technique can be used to compare representational geometries of neural models . it does not require large training samples and is not prone to overfitting, authors say . |
Text Genre and Training Data Size in Human-like Parsing (D19-1)
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| Challenge: | Using domain-specific training, NLP systems work better, but only when the training examples come from the same textual genre. |
| Approach: | They relate the states of a neural phrase-structure parser to electrophysiological measures from human participants. |
| Outcome: | The proposed model is well-matched to the training data from human participants, but only when the training examples come from the same genre. |
Feature2Vec: Distributional semantic modelling of human property knowledge (D19-1)
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| Challenge: | Existing distributional semantic models of word meaning are limited in size due to constraints associated with exhaustively listing properties for large numbers of words. |
| Approach: | They propose a method for mapping human property knowledge onto a distributional semantic space and adapt it to the task of modelling concept features. |
| Outcome: | The proposed model performs better on evaluation tasks and improves on other evaluation tasks. |
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation (D19-1)
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| Challenge: | interacting with a model for Visual Question Answering (VQA) quickly reveals that these models lack consistency. |
| Approach: | They propose a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. |
| Outcome: | The proposed data augmentation module improves the consistency of VQA models on the Con-VQA dataset and is a strong baseline for future research. |
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)
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| Challenge: | SQA is an emerging application of NLP in the medical, geography, and legal domains. |
| Approach: | They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level. |
| Outcome: | The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level. |
Revisiting the Evaluation of Theory of Mind through Question Answering (D19-1)
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| Challenge: | Existing benchmarks for theory of mind are flawed due to dataset biases . evaluators have been using the Sally-Anne test to infer false beliefs in others . |
| Approach: | They propose to use question answering to evaluate theory of mind . they propose to explicitly control for data regularities via a careful examination of the answer space . |
| Outcome: | The proposed evaluation protocol and dataset control for data regularities via a careful examination of the answer space. |
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (D19-1)
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| Challenge: | Existing studies have shown that BERT models can find answers from multiple passages . however, the results of these studies are still unaddressed. |
| Approach: | They propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question. |
| Outcome: | The proposed model outperforms state-of-the-art models on four benchmarks. |
A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering (D19-1)
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| Challenge: | Existing approaches for non-factoid question answering can be categorized into representation and interaction focused approaches. |
| Approach: | They propose a novel approach which derives contextualized uni-gram representation from n-grams. |
| Outcome: | The proposed approach achieves state-of-the-art in two public non-factoid question answering datasets. |
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network (D19-1)
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| Challenge: | Recent large-scale datasets specify that external knowledge is required to answer questions. |
| Approach: | They propose a model that leverages external knowledge to construct sub-graphs for entities in machine comprehension context. |
| Outcome: | The proposed model achieves state-of-the-art performance on the ReCoRD dataset. |
Answering Conversational Questions on Structured Data without Logical Forms (D19-1)
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| Challenge: | Existing approaches to answering sequential questions based on structured objects do not use a logical form as an intermediate representation. |
| Approach: | They propose a novel approach to answering sequential questions based on structured objects without using a logical form as an intermediate representation. |
| Outcome: | The proposed approach is competitively tested on the Sequential Question Answering (SQA) task. |
Improving Answer Selection and Answer Triggering using Hard Negatives (D19-1)
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| Challenge: | Existing approaches to answer selection and answer triggering have been proposed. |
| Approach: | They propose to use hard negatives with a siamese network and a suitable loss function for answer selection and answer triggering. |
| Outcome: | The proposed model improves on InsuranceQA, SelQA, and an internal QA dataset by 2.3 points over previous baselines. |
Can You Unpack That? Learning to Rewrite Questions-in-Context (D19-1)
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| Challenge: | Existing QA datasets lack key NLP problems like coreference and ellipsis resolution. |
| Approach: | They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer. |
| Outcome: | The proposed task is based on a dataset of 40,527 questions based in QuAC . it requires models to link questions together to resolve conversational dependencies . |
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (D19-1)
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| Challenge: | Existing reading comprehension benchmarks do not contain complex coreferential phenomena . obtaining questions focused on such phenomena is difficult because of lexical cues . |
| Approach: | They propose to use a crowdsourced dataset to examine the ability of models to resolve coreference among entities in Wikipedia paragraphs. |
| Outcome: | The proposed model performs significantly worse than humans on the reading comprehension benchmark . paragraphs and other longer texts typically make multiple references to the same entities . |
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model (D19-1)
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| Challenge: | Existing studies on RC datasets in English have limited results due to lack of training data. |
| Approach: | They systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with pre-trained language representation model. |
| Outcome: | The proposed model performs well on reading comprehension tasks on pre-trained language representation models. |
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions (D19-1)
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| Challenge: | Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships. |
| Approach: | They introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. |
| Outcome: | The proposed dataset is the first open-domain dataset for reasoning about qualitative relationships. |
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (D19-1)
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| Challenge: | End-to-end reading comprehension models have been successful at extracting text answers, but there are still problems with generalizing them to abstractive numerical reasoning. |
| Approach: | They propose to augment a BERT-based reading comprehension model with a set of executable ‘programs’ which encompass simple arithmetic as well as extraction. |
| Outcome: | The proposed model can perform 33% absolute improvement on the DROP dataset, with very few training examples. |
A Gated Self-attention Memory Network for Answer Selection (D19-1)
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| Challenge: | Existing deep learning approaches for answer selection use word-level comparison followed by aggregation. |
| Approach: | They propose a new gated self-attention memory network for answer selection task . they combine a transfer learning technique from a large-scale online corpus to create a gated network . |
| Outcome: | The proposed model outperforms existing methods on two standard answer selection datasets: TrecQA and WikiQA. |
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets (D19-1)
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| Challenge: | Paraphrase generation is an interesting and challenging task which has numerous practical applications. |
| Approach: | They analyze datasets commonly used for paraphrase generation research and show that simply parroting input sentences surpasses state-of-the-art models when evaluated on standard metrics. |
| Outcome: | The proposed model can generate paraphrases even without making any changes to the input sentence or even none at all, compared with other models. |
Query-focused Sentence Compression in Linear Time (D19-1)
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| Challenge: | Existing techniques for constrained compression are slow and require third-party solvers. |
| Approach: | They propose a query-focused sentence compression technique which constructs length and lexically constrained compressions in linear time by growing a subgraph in the dependency parse of a sentence. |
| Outcome: | The proposed technique achieves an 11x empirical speedup over baseline methods while improving query-focused applications. |
Generating Personalized Recipes from Historical User Preferences (D19-1)
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| Challenge: | Existing methods to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. |
| Approach: | They propose to expand a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. |
| Outcome: | The proposed model generates plausible recipes from user-aware representations of recipes from 180K recipes and 700K interactions. |
Generating Highly Relevant Questions (D19-1)
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| Challenge: | Existing neural QG models generate generic questions that are not relevant to passages and answers. |
| Approach: | They propose to prioritize words that are morphologically close to words in the passage when generating questions. |
| Outcome: | The proposed methods improve relevance of generated questions to passages and answers. |
Improving Neural Story Generation by Targeted Common Sense Grounding (D19-1)
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| Challenge: | Recent advances in language modeling have yielded thematic and stylistic coherence in story generation through large scale pretraining of Transformer models. |
| Approach: | They propose a multi-task learning scheme to achieve better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. |
| Outcome: | The proposed model achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts dataset. |
Abstract Text Summarization: A Low Resource Challenge (D19-1)
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| Challenge: | Existing datasets for multilingual text summarization are difficult to construct and lack of human knowledge and language processing abilities in computers makes text summaries a challenging task. |
| Approach: | They propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language. |
| Outcome: | The proposed system improves on the development and test sets on the German language text using the state-of-the-art “Transformer” model. |
Generating Modern Poetry Automatically in Finnish (D19-1)
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| Challenge: | a novel approach to generate poetry for the morphologically rich Finnish language is presented . the method is evaluated and described within the paradigm of computational creativity . |
| Approach: | They propose a method for generating poetry automatically for the morphologically rich Finnish language using a genetic algorithm. |
| Outcome: | The proposed method improves the state-of-the-art of previous Finnish poetry generators by introducing a higher degree of freedom in terms of structural creativity. |
SUM-QE: a BERT-based Summary Quality Estimation Model (D19-1)
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| Challenge: | SUM-QE is a quality estimation model for summarization that captures linguistic qualities that traditional evaluation metrics fail to capture. |
| Approach: | They propose a new quality estimation model based on BERT that addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation without comparison with human ratings. |
| Outcome: | The proposed model outperforms existing models on linguistic quality aspects that are only indirectly captured by content-based summarization evaluations without comparison with human ratings. |
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods to generate paraphrases are not trivial and often fail in practice. |
| Approach: | They propose to use imitation learning to boost the performance of generating paraphrases by using a pointer-generator model. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the benchmark datasets. |
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses (D19-1)
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| Challenge: | Sentence position is a strong feature for news summarization, since the lead often summarizes the key points of the article. |
| Approach: | They propose two techniques to make neural systems sensitive to the importance of content in different parts of the article by using random shuffled sentences to pretrain the model. |
| Outcome: | The proposed techniques improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining. |
Learning Rhyming Constraints using Structured Adversaries (D19-1)
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| Challenge: | Existing approaches to text generation fail to capture higher-level structure in text, for example, rhyming patterns. |
| Approach: | They propose a method that uses a structured discriminator to learn rhyming constraints from poetry . the discriminator compares two English poetry datasets based on a learned similarity matrix . |
| Outcome: | The proposed method can learn rhyming patterns in English poetry without explicit phonetic information. |
Question-type Driven Question Generation (D19-1)
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| Challenge: | Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name . |
| Approach: | They propose to automatically predict the question type based on the input answer and context. |
| Outcome: | The proposed model improves on both SQuAD and MARCO datasets and improves accuracy on the input answer and context. |
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization (D19-1)
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| Challenge: | Abstractive summarization tasks are often based on deep reinforcement learning (RL) but the traditional reward system Rouge-L simply looks for exact n-grams matches between candidates and annotated references, which makes the generated sentences repetitive and incoherent. |
| Approach: | They propose to use distributional semantics to measure matching degrees instead of Rouge-L to generate sentences with n-grams matches. |
| Outcome: | The proposed reward has superiority over the existing reward, despite the incoherence of the generated sentences. |
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation (D19-1)
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| Challenge: | Existing deep learning approaches for text-to-SQL generation are limited to the WikiSQl dataset . a novel clause-wise decoding neural network model can be used to generate complex queries over multiple databases . |
| Approach: | They propose a SQL clause-wise decoding neural architecture with a schema encoder to address the Spider task. |
| Outcome: | The proposed model achieves 4.6% accuracy gain on the Spider dataset and 9.8% accuracy gain in test and dev sets. |
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects (D19-1)
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| Challenge: | a dataset of human judgments is used to test the ability to construct models with an understanding of commonsense knowledge. |
| Approach: | They crowdsource sentences that answer a question about adjectives and their transitivity . they build strong baselines for the task using a classification approach . |
| Outcome: | The proposed model outperforms word-level models on commonsense reasoning tasks. |
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching (D19-1)
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| Challenge: | Recent work on text sequence matching tasks uses task specific supervised datasets, which are always limited to the amount due to the cost of annotation. |
| Approach: | They propose an aggregation method to combine Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. |
| Outcome: | The proposed model improves on two publicly available datasets, WikiQA and SNLI. |
What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition (D19-1)
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| Challenge: | Contextualized word embeddings have boosted many NLP tasks compared with static word embeds. |
| Approach: | They propose a framework that can explain word meanings given contextualized word embeddings for better interpretation. |
| Outcome: | The proposed framework can explain word meanings given contextualized word embeddings for better interpretation. |
Pre-Training BERT on Domain Resources for Short Answer Grading (D19-1)
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| Challenge: | Pre-trained contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. |
| Approach: | They propose to augment domain-specific data by using labeled short answering grading data for further enhancement of the pre-trained language model. |
| Outcome: | The proposed model can be enhanced by augmenting data from domain-specific resources like textbooks and labeled short answering grading data. |
WIQA: A dataset for “What if...” reasoning over procedural text (D19-1)
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| Challenge: | a dataset of “What if...” questions is available for procedural text comprehension . we present the dataset as an open challenge to the community . |
| Approach: | They propose a dataset of “What if...” questions over procedural text . they use paragraphs annotated with multiple influence graphs to create the questions . |
| Outcome: | The proposed dataset achieves 73.8% accuracy, well below the human performance of 96.3%. |
Evaluating BERT for natural language inference: A case study on the CommitmentBank (D19-1)
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| Challenge: | Natural language inference datasets can identify premise-hypothesis relationship without observing premise . recasting of the CommitmentBank for NLI creates hypotheses that stand in entailment/contradiction/neutral relationship with premise. |
| Approach: | They propose to recast the CommitmentBank for NLI to stand in certain relationships with the premise . hypotheses are complements of clause-embedding verbs in each premise, rethinking the CommittedBank . |
| Outcome: | The proposed model performs well on the CommitmentBank with 85% F1 . however, the model does not capture the full complexity of pragmatic reasoning, authors say . |
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (D19-1)
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| Challenge: | Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain. |
| Approach: | They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task. |
| Outcome: | The proposed approach improves the baseline BioELMo architecture for the Medical NLI task. |
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English (D19-1)
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Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc’Aurelio Ranzato
| Challenge: | a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. |
| Approach: | They propose to use a dataset to evaluate methods trained on low-resource language pairs . they report baseline performance using supervised, weakly supervised and semi-supervised settings . |
| Outcome: | The proposed evaluation datasets show that current state-of-the-art methods perform poorly on this benchmark, posing a challenge to the research community working on low-resource MT. |
Mask-Predict: Parallel Decoding of Conditional Masked Language Models (D19-1)
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| Challenge: | a masked language model is used to train a model to predict subsets of mangled words . a parallel decoding algorithm can be used to generate translations in a constant number of iterations. |
| Approach: | They propose a model and a parallel decoding algorithm which train a machine to predict any subset of target words . they introduce conditional masked language models (CMLMs) which are trained with a mangled language model objective . |
| Outcome: | The proposed model improves state-of-the-art performance levels for non-autoregressive and parallel decoding models by over 4 BLEU on average. |
Learning to Copy for Automatic Post-Editing (D19-1)
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| Challenge: | Automatic post-editing (APE) is an important task in natural language processing. |
| Approach: | They propose a method that explicitly models how to copy words from a machine translation to a correct translation. |
| Outcome: | The proposed method outperforms all published methods on the WMT 2016-2017 datasets. |
Exploring Human Gender Stereotypes with Word Association Test (D19-1)
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| Challenge: | Existing word embeddings have been used to study gender stereotypes in texts . however, evaluating their validities is still an open problem . et al.: this study investigates gender bias using the lens of language, especially, the words . |
| Approach: | They use word association test to derive bias scores for large amount of words . they find that these bias scores correlate well with bias in the real world . |
| Outcome: | The proposed method correlates well with bias in the real world, and with census data, it provides a different perspective on gender stereotypes in words. |
A Modular Architecture for Unsupervised Sarcasm Generation (D19-1)
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| Challenge: | Existing systems for sarcasm generation are elusive due to the fact that both selection of contents and training of sarcasm are based on the same data. |
| Approach: | They propose a framework that takes a literal negative opinion as input and translates it into a sarcastic version. |
| Outcome: | The proposed system outperforms baselines built using known unsupervised statistical and neural machine translation and style transfer techniques. |
Generating Classical Chinese Poems from Vernacular Chinese (D19-1)
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| Challenge: | Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems. |
| Approach: | They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors . |
| Outcome: | The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems. |
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes (D19-1)
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| Challenge: | a neural architecture learns to generate content in a specific order without explicit specifications of the relations between input entities and output entities. |
| Approach: | They propose a natural language generation task that generates discharge instructions from ICD codes . they propose to model content ordering and text generation in a specific order . |
| Outcome: | The proposed model outperforms baseline models in BLEU scores and human evaluation. |
Constraint-based Learning of Phonological Processes (D19-1)
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| Challenge: | Phonological processes govern the way speech sounds in natural languages change depending on context . a novel approach to learning phonological processes from related utterances is proposed . |
| Approach: | They propose an unsupervised approach to learning phonological processes from related utterances . they encode the problem into Boolean constraints that enable data efficiency and fast inference . |
| Outcome: | The proposed approach achieves high accuracy at interactive speeds on phonology problems and datasets. |
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)
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| Challenge: | Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity. |
| Approach: | They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families. |
| Outcome: | The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets. |
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation (D19-1)
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| Challenge: | Existing fine-grained entity typing models are criticized for label independence assumption . |
| Approach: | They propose a fine-grained entity typing model with a new attention mechanism and a hybrid type classifier to exploit type inter-dependency with latent type representation. |
| Outcome: | The proposed model significantly advances the state-of-the-art on fine-grained entity typing. |
An Improved Neural Baseline for Temporal Relation Extraction (D19-1)
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| Challenge: | Existing datasets are small and/or have low inter-annotator agreements. |
| Approach: | They propose a new neural system that achieves 10% absolute accuracy improvement over the previous best system. |
| Outcome: | The proposed system achieves 10% absolute improvement over the previous best system on two benchmark datasets. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
| Approach: | They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing. |
| Outcome: | The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets. |
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities (D19-1)
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| Challenge: | Named entity recognition (NER) aims at identifying shallow semantic elements in text. |
| Approach: | They propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing the problem into two subtasks. |
| Outcome: | The proposed model achieves state-of-the-art in a standard dataset even without external features. |
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas (D19-1)
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| Challenge: | Existing approaches to extract n-ary relations from text are limited to binary relations. |
| Approach: | They propose to learn relation representations of lower-arity facts from decomposing higher-arities . they conduct experiments with datasets for ternary relation extraction . |
| Outcome: | The proposed method improves the performance of n-ary relation extraction methods compared to previous methods. |
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers . |
| Outcome: | The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark. |
“A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia (D19-1)
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| Challenge: | Vossian Antonomasia is a stylistic device which attributes a property to a person by naming another person as a reference point. |
| Approach: | They propose a method for the extraction of Vossian Antonomasias that works completely automatically . they use named entity recognition, distant supervision and a bi-directional LSTM . |
| Outcome: | The proposed method outperforms the only existing semi-automatic method for VA identification by more than 30 percentage points in precision. |
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the important basic technologies for Natural Language Processing (NLP) . |
| Approach: | They propose to use long short-term memory (LSTM) of NER model to capture chemical com- pound paraphrases by sharing parameters of LSTM and character embeddings be- tween the two models. |
| Outcome: | The proposed method improves chemi- cal NER and achieves state-of-the-art performance on the BioCreative IV’s CHEMDNER task. |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
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| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |
ner and pos when nothing is capitalized (D19-1)
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| Challenge: | Named entity recognition and part of speech tagging require capitalization in training. |
| Approach: | They propose to modify only the casing of the train or test data using lowercasing and truecasing methods to modify the cassing of a model with high performance on both cased and uncased text. |
| Outcome: | The proposed model improves mention detection on noisy out-of-domain Twitter data by 8%. |
CaRB: A Crowdsourced Benchmark for Open IE (D19-1)
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| Challenge: | Open Information Extraction (Open IE) systems have been evaluated traditionally via manual annotation. |
| Approach: | They propose to use a dataset to score Open IE systems by matching system predictions with benchmark datasets. |
| Outcome: | The proposed framework matches predictions with the benchmark dataset and is noisy and inconsistent. |
Weakly Supervised Attention Networks for Entity Recognition (D19-1)
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| Challenge: | Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain. |
| Approach: | They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels. |
| Outcome: | The proposed model performs reasonably well on the task of entity recognition despite not having access to token-level ground truth data. |
Revealing and Predicting Online Persuasion Strategy with Elementary Units (D19-1)
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| Challenge: | Existing studies have examined persuasive discourses with regard to dynamics or lexical features. |
| Approach: | They propose to annotate five types of EUs in a persuasive forum and propose a baseline neural model that identifies the EU boundary and type. |
| Outcome: | The proposed model reveals that EUs definitively characterize online persuasive strategies. |
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
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| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
Learning with Noisy Labels for Sentence-level Sentiment Classification (D19-1)
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| Challenge: | Existing research on learning with noisy labels dates back to the 1980s, but it is still vibrant today. |
| Approach: | They propose a novel DNN model called NetAb to deal with noisy labels during training and train the networks using their respective loss functions in mutual reinforcement. |
| Outcome: | The proposed model can fit training data with noisy labels and predict clean labels. |
DENS: A Dataset for Multi-class Emotion Analysis (D19-1)
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| Challenge: | Existing sentence-level methods for emotion analysis are limited by the number of words in tweets and product reviews. |
| Approach: | They introduce a dataset for multi-class emotion analysis from long-form narratives in English . they use classic literature and modern online narratives available on Wattpad . |
| Outcome: | The proposed dataset provides a novel opportunity for emotion analysis that requires moving beyond sentence-level techniques. |
Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
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| Challenge: | Recent studies show improvements in stance detection by using attention mechanism or sentiment information. |
| Approach: | They propose a multi-task framework that incorporates attention mechanism and takes sentiment classification as an auxiliary task. |
| Outcome: | The proposed model outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset. |
A Robust Self-Learning Framework for Cross-Lingual Text Classification (D19-1)
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| Challenge: | Recent advances in pretrained contextual representation models have made significant progress on a number of different English NLP tasks. |
| Approach: | They propose a robust framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |
| Outcome: | The proposed framework includes unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora (D19-1)
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| Challenge: | Existing methods for flipping sentiment are costly and require parallel data. |
| Approach: | They propose a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose 'sensational' model that learns to minimize sentiment and content losses in a fully end-to-end manner. |
| Outcome: | The proposed model offers well-balanced results across Yelp restaurant and Amazon product reviews. |
Label Embedding using Hierarchical Structure of Labels for Twitter Classification (D19-1)
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| Challenge: | Twitter is used for disaster monitoring and news material gathering . we propose a method that can consider the hierarchical structure of labels and labels themselves . |
| Approach: | They propose a method that can consider the hierarchical structure of labels and label texts themselves. |
| Outcome: | The proposed method outperforms the methods of the conference participants over the text REtrieval Conference (TREC) 2018 Incident Streams (IS) dataset. |
Interpretable Word Embeddings via Informative Priors (D19-1)
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| Challenge: | Existing word embeddings lack interpretability and are unsupervised . this limitation limits their use within computational social science and digital humanities. |
| Approach: | They propose to use informative priors to create interpretable dimensions for probabilistic word embeddings using a priori model. |
| Outcome: | The proposed models capture latent semantic concepts better than or on-par with the current state of the art while maintaining the simplicity and generalizability of priors. |
Adversarial Removal of Demographic Attributes Revisited (D19-1)
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| Challenge: | Several approaches have been proposed to learn classifiers that are invariant (unbiased with respect) to protected attributes. |
| Approach: | They propose to use a diagnostic classifier trained on a held-out subsample to find protected attributes for mention detection at above-chance levels. |
| Outcome: | The proposed classifier generalizes poorly to new in-domain and new domains, suggesting it relies on correlations specific to their particular data sample. |
A deep-learning framework to detect sarcasm targets (D19-1)
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
Incorporating Label Dependencies in Multilabel Stance Detection (D19-1)
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| Challenge: | Stance detection is a well-studied task in social media, but previous work focused on multiclass versions of the problem where labels are mutually exclusive. |
| Approach: | They propose a method that explicitly incorporates label dependencies in the training objective and reduces multilabel to multiclass learning. |
| Outcome: | The proposed method improves on two out of three datasets and reduces multilabel to multiclass learning. |
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts (D19-1)
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| Challenge: | a recent study shows that sports broadcasters build drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. |
| Approach: | They use FOOTBALL to examine racial bias in sports commentary . they identify major confounding factors for researchers examining rraecial bias . |
| Outcome: | The proposed dataset supports previous social science studies on commentator bias . it contains 1,455 broadcast football transcripts annotated with 250K player mentions and racial metadata . |
Charge-Based Prison Term Prediction with Deep Gating Network (D19-1)
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| Challenge: | Existing work merely predicts the total prison term, but in reality a defendant is often charged with multiple crimes. |
| Approach: | They propose a charge-based prison term prediction task that better fits real needs and makes it more accurate and interpretable. |
| Outcome: | The proposed method achieves state-of-the-art performance for charge-specific feature selection and aggregation. |
Restoring ancient text using deep learning: a case study on Greek epigraphy (D19-1)
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| Challenge: | illegible parts of ancient texts must be restored by specialists, known as epigraphists, using deep neural networks to recover missing characters from text input. |
| Approach: | They propose a model that recovers missing characters from a damaged text input using deep neural networks. |
| Outcome: | The proposed model achieves a 30.1% character error rate, compared to the 57.3% of human epigraphists. |
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition (D19-1)
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| Challenge: | Existing methods for humor recognition require a large amount of training data with labels to learn effective features. |
| Approach: | They propose a tensor embedding method that can extract lexical humor features for continuous humor recognition by using word-word co-occurrence to encode contextual content of documents, and then decompose the tenor to get corresponding vector representations. |
| Outcome: | The proposed method achieves a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B, but without the need for any external training corpus. |
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (D19-1)
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| Challenge: | Existing data augmentation techniques for text classification are difficult to implement and cost a high amount of money. |
| Approach: | They propose to use four simple but powerful operations to boost performance on text classification tasks to improve synonym replacement, random insertion, random swap, and random deletion. |
| Outcome: | The proposed techniques improve performance on five classification tasks and are particularly useful for smaller datasets. |
Neural News Recommendation with Multi-Head Self-Attention (D19-1)
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| Challenge: | Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. |
| Approach: | They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news. |
| Outcome: | The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news. |
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)
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| Challenge: | Humor plays important role in human communication, which makes it important problem for natural language processing. |
| Approach: | They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset . |
| Outcome: | The proposed scheme gives scenarios of how humor arises in text . it contains key words that trigger humor, character relationship, scene, and humor categories . |
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints (D19-1)
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| Challenge: | Existing methods for creating anagrams do not pay much attention to the naturalness of the generated anagramms. |
| Approach: | They propose to combine depth-first search with modern neural language models to generate anagrams by permutation of characters in an input sentence or phrase. |
| Outcome: | The proposed method generates significantly more natural anagrams than baseline methods. |
STANCY: Stance Classification Based on Consistency Cues (D19-1)
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| Challenge: | Recent work has shown that stance classification is a critical step for information credibility and automated fact-checking. |
| Approach: | They propose a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. |
| Outcome: | The proposed model outperforms existing methods on a Perspectrum dataset and shows that it is more accurate than existing methods. |
Cross-lingual intent classification in a low resource industrial setting (D19-1)
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| Challenge: | Recent advances in multilingual text representations promise cross-lingual transfer for classifiers . but practical application calls for a combination of techniques for useful results . |
| Approach: | They investigate different approaches to multilingual intent classification in a low resource setting. |
| Outcome: | The proposed methods show promise, but practical application calls for a combination of techniques. |
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence (D19-1)
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| Challenge: | Existing models that generate semantically correct regular expressions from NLs are not yet fully understood. |
| Approach: | They propose a model that rewards reinforcement learning based on the semantic equivalence between two regular expressions. |
| Outcome: | The proposed model reduces training time and produces state-of-the-art results on three benchmark datasets. |
Using Clinical Notes with Time Series Data for ICU Management (D19-1)
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| Challenge: | Existing work on monitoring patients in ICU has focused on using time series signals from medical instruments. |
| Approach: | They propose to add clinical notes to the time-series data to improve model performance for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting. |
| Outcome: | The proposed model improves on three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting. |
Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary (D19-1)
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| Challenge: | a machine foreign-language teacher replaces word tokens with glosses in a foreign language to ease the human reader into understanding the L2 vocabulary. |
| Approach: | They propose a machine foreign-language teacher that modifies text by replacing word tokens with glosses in a foreign language to ease the human reader into understanding the L2 . |
| Outcome: | The proposed model can learn representations for novel words and is a proxy for word guessing and learning ability of real human students. |
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature (D19-1)
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| Challenge: | a complex socio-political system is causing problems such as food insecurity . a first step is to extract past interventions and when and where they have been applied . |
| Approach: | They develop an automatic extraction system to extract past interventions from texts . they analyze a corpus annotated with interventions to foster research . |
| Outcome: | The proposed system extracts past interventions and when and where they have been applied from text . it shows early, encouraging results on extracting interventions . |
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation (D19-1)
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| Challenge: | Existing work on map-based NL navigation relies on small artificial worlds with a fixed set of entities known in advance. |
| Approach: | They propose a task to interpret navigation instructions in natural language (NL) they use a dataset aligned with real, dense, urban maps to study neural architectures . |
| Outcome: | The proposed task is based on a dataset of 2515 navigation instructions aligned with real routes over three regions of Manhattan. |
Context-Aware Conversation Thread Detection in Multi-Party Chat (D19-1)
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| Challenge: | In multi-party chat, it is common for multiple conversations to occur concurrently . a new model that automatically disentangles conversation threads is proposed . |
| Approach: | They propose a Context-Aware Thread Detection model that automatically disentangles conversation threads in chat logs. |
| Outcome: | The proposed model outperforms state-of-the-art models on four real-world chat logs. |