Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Privacy-preserving Neural Representations of Text (D18-1)
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| Challenge: | a specific type of attack is used to characterize the privacy of neural representations for NLP tasks, in the context of privacy protection. |
| Approach: | They propose several defense methods based on modified training objectives and characterize the tradeoff between privacy and the utility of neural representations. |
| Outcome: | The proposed defenses improve the privacy of neural representations and characterize the tradeoff between privacy and utility of representations. |
Adversarial Removal of Demographic Attributes from Text Data (D18-1)
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| Challenge: | Recent advances in Representation Learning and Adversarial Training remove unwanted features from the learned representation. |
| Approach: | They show that demographic information of authors is encoded in the intermediate representations learned by text-based neural classifiers. |
| Outcome: | The proposed approach achieves higher accuracies on the same dataset, the authors show . they show that the proposed approach is effective in removing unwanted features from the learned representations. |
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (D18-1)
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| Challenge: | Recent work on automated fact-checking does not consider external evidence, but requires rich lexicons. |
| Approach: | They propose a neural network model that aggregates external evidence and language . they also derive informative features for generating user-comprehensible explanations . |
| Outcome: | The proposed model aggregates signals from external evidence articles, language and trustworthiness of their sources without human intervention. |
It’s going to be okay: Measuring Access to Support in Online Communities (D18-1)
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| Challenge: | Despite substantial efforts to reduce gender disparities in online social contexts, gender gaps persist and negatively affect women through online harassment. |
| Approach: | They propose a new dataset and method for identifying supportive replies and new methods for inferring gender from text and name to examine the disparity in support across millions of online interactions. |
| Outcome: | The proposed model shows that identifying as a woman is associated with higher rates of support, but also higher rates disparagement. |
Detecting Gang-Involved Escalation on Social Media Using Context (D18-1)
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Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown
| Challenge: | In cities such as Chicago, gang-involved youth have increasingly turned to social media to post about their experiences and intents online. |
| Approach: | They propose a system that uses domain-specific resources and contextual representations of the emotional and semantic content of the user’s recent tweets and their interactions with other users to detect Aggression and Loss in social media posts. |
| Outcome: | The proposed system improves on a large unlabeled dataset and incorporates contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. |
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge (D18-1)
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| Challenge: | Recent work has shown impressive progress in comprehending procedural text, but their predictions can be inconsistent or highly improbable. |
| Approach: | They propose to incorporate global constraints and bias reading with corpora-based preferences to improve the predicted effects of actions in a paragraph. |
| Outcome: | The proposed model significantly outperforms earlier models on a benchmark dataset for procedural text comprehension (+8% relative gain) it avoids nonsensical predictions that earlier models make, and it is more robust than previous models. |
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (D18-1)
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Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
| Challenge: | a plethora of new natural language inference datasets has been created in recent years . however, these datasets do not provide clear insight into what type of reasoning or inference a model may be performing. |
| Approach: | They propose to recast 13 existing natural language inference datasets into a common structure. |
| Outcome: | The proposed datasets provide insight into how well a sentence representation captures distinct types of reasoning. |
Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (D18-1)
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| Challenge: | Existing methods to extract information from text do not capture disparity between demographic groups. |
| Approach: | They propose a task of Textual Analogy Parsing to model higher-order meanings by comparing poverty rates between different demographic groups. |
| Outcome: | The proposed model can be used to generate graphs from quantitative text. |
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (D18-1)
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| Challenge: | a new dataset presents a task of grounded commonsense inference, unifying natural language inference and commonsensical reasoning. |
| Approach: | They propose a procedure that constructs a de-biased dataset by iteratively training stylistic classifiers and using them to filter the data. |
| Outcome: | The proposed procedure oversamples a de-biased dataset using state-of-the-art language models . human models struggle on the proposed procedure, indicating significant opportunities for future research. |
TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification (D18-1)
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| Challenge: | Existing methods to determine whether a claim is supported by evidence are decoupled from determining the truth value of the claim. |
| Approach: | They propose a system that decouples evidence finding from determining the truth value of a claim . they propose identifying appropriate evidence for a given claim and determining its truth value . |
| Outcome: | The proposed system decouples evidence finding from determining the truth value of a claim . it can identify evidence candidates and determine the truth of the claim based on predicted evidence . |
Associative Multichannel Autoencoder for Multimodal Word Representation (D18-1)
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| Challenge: | Existing models that represent word meanings from word co-occurrences ignore associations between modalities and lack ability to transfer information between . |
| Approach: | They propose a novel associative multichannel autoencoder that integrates textual, visual and auditory inputs to learn multimodal word representations. |
| Outcome: | The proposed model outperforms strong unimodal models and state-of-the-art models on six benchmark concepts similarity tests. |
Game-Based Video-Context Dialogue (D18-1)
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| Challenge: | Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. |
| Approach: | They propose to use live soccer game videos and Twitch.tv chats to develop visual-grounded dialogue models. |
| Outcome: | The proposed model can generate relevant temporal and spatial event language from live video and chat history while also being relevant to chat history. |
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions (D18-1)
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| Challenge: | Existing approaches to image captioning combine visual and semantic attention to generate a detailed and comprehensive caption. |
| Approach: | They propose a stepwise image-topic merging network that integrates visual and semantic attentions to generate a detailed caption. |
| Outcome: | The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performance. |
Multimodal Language Analysis with Recurrent Multistage Fusion (D18-1)
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| Challenge: | Comprehending multimodal language requires modeling interactions between modalities and between them. |
| Approach: | They propose a multistage fusion network which decomposes the fusion problem into multiple stages, each focused on a subset of multimodal signals for specialized, effective fusion. |
| Outcome: | The proposed model performs state-of-the-art across three datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. |
Temporally Grounding Natural Sentence in Video (D18-1)
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| Challenge: | Existing methods for grounding natural sentences in video are limited to a single pass. |
| Approach: | They propose a Temporal GroundNet (TGN) method that captures the evolving fine-grained frame-by-word interactions between video and sentence to ground the segment corresponding to the sentence. |
| Outcome: | The proposed method significantly improves on the state-of-the-art methods on three public datasets and shows significant improvements in performance. |
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)
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| Challenge: | Existing methods for coreference resolution are based on word2vec-like representations of entities. |
| Approach: | They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers . |
| Outcome: | The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo . |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers (D18-1)
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| Challenge: | Recent coreference resolvers have notable improvements on the CoNLL evaluation sets, but struggle to generalize properly to new datasets. |
| Approach: | They investigate the role of linguistic features in building more generalizable coreference resolvers . they show that employing features and subsets of their values that are informative for coreference resolution improves generalization . |
| Outcome: | The proposed system achieves state-of-the-art results on WikiCoref, compared with a system trained on CoNLL. |
Neural Segmental Hypergraphs for Overlapping Mention Recognition (D18-1)
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| Challenge: | Existing models that assume that mentions are non-overlapping spans in text may not perform well in practice. |
| Approach: | They propose a segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. |
| Outcome: | The proposed representation achieves state-of-the-art performance in three benchmark datasets annotated with overlapping mentions. |
Variational Sequential Labelers for Semi-Supervised Learning (D18-1)
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| Challenge: | a family of multitask variational methods for semi-supervised sequence labeling is currently unclear how to use them in the context of sequence labelling. |
| Approach: | They propose a family of multitask variational methods for semi-supervised sequence labeling using latent variables and a discriminative labeler. |
| Outcome: | The proposed models outperform standard sequential baselines on 8 sequence labeling datasets and improve further with unlabeled data. |
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
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| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance (D18-1)
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| Challenge: | a framework for cross-domain and cross-language transfer has hardly been explored . cross-linguistic and cross language transfer methods are used for multilingual applications . |
| Approach: | They propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks and bilingual word embeddings to train a model on labeled data from one language pair. |
| Outcome: | The proposed model outperforms existing models even when trained in the lazy setup . the proposed model can be applied to nine English-German and nine English - french domain pairs without retraining . |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
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| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |
Unsupervised Multilingual Word Embeddings (D18-1)
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| Challenge: | Prior art for learning UMWEs relies on a number of independently trained UBWEs to obtain multilingual embeddings. |
| Approach: | They propose a fully unsupervised framework that exploits the relations between all language pairs to learn multilingual embeddings without cross-lingual supervision. |
| Outcome: | The proposed framework outperforms supervised approaches on multilingual word translation and cross-lingual word similarity and beats a number of other approaches trained with cross-linguistic resources. |
CLUSE: Cross-Lingual Unsupervised Sense Embeddings (D18-1)
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| Challenge: | Existing models for learning bilingual sense embeddings that encode semantics are weak for recognizing multi-sense word representations . elucidation of word embeddables is difficult because they do not allow a word to have different meanings in different contexts. |
| Approach: | They propose a sense induction and representation learning model that learns bilingual sense embeddings that align well in the vector space. |
| Outcome: | The proposed model shows that the learned embeddings are aligned well in the vector space. |
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization (D18-1)
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| Challenge: | Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge to accentuate a particular semantic relation in the specialized vector space. |
| Approach: | They propose a method for specializing distributional word vectors using external lexical knowledge. |
| Outcome: | The proposed method improves on word similarity, dialog state tracking, and lexical simplification across three languages and on three tasks. |
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
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| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)
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| Challenge: | a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence. |
| Approach: | They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence. |
| Outcome: | The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text. |
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling (D18-1)
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
| Approach: | They propose to use a full-vocabulary setup to test the performance of language modeling (LM) on 50 typologically diverse languages. |
| Outcome: | The proposed language modeling task is based on a full vocabulary setup focused on word-level prediction on 50 typologically diverse languages. |
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text (D18-1)
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| Challenge: | a feed-forward network can label codemixed and monolingual text in 100 languages and 100 language pairs. |
| Approach: | They propose a feed-forward network that can provide a language code for every token in a sentence . they show that the model can label both codemixed and monolingual text in 100 languages . |
| Outcome: | The proposed model outperforms previous multilingual approaches in terms of accuracy and speed. |
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning (D18-1)
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| Challenge: | Existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. |
| Approach: | They propose to use user-attention-based Convolutional Neural Networks to capture individuality and opinion bias in microblog posts and a novel adversarial cross-lingual learning framework to enrich the user post representation. |
| Outcome: | The proposed method outperforms state-of-the-art baseline algorithms with large margins on English and Chinese microblog datasets. |
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)
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| Challenge: | Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective. |
| Approach: | They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space . |
| Outcome: | The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches. |
Cross-lingual Lexical Sememe Prediction (D18-1)
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| Challenge: | Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems . |
| Approach: | They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. |
| Outcome: | The proposed model improves on baseline methods on real-world datasets. |
Neural Cross-Lingual Named Entity Recognition with Minimal Resources (D18-1)
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| Challenge: | Named-entity recognition (NER) models are highly dependent on large amounts of labeled data. |
| Approach: | They propose a method that finds translations based on bilingual word embeddings . they also propose 'self-attention' which allows for a degree of flexibility with respect to word order . |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on common languages with lower resource requirements than previous approaches. |
A Stable and Effective Learning Strategy for Trainable Greedy Decoding (D18-1)
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| Challenge: | Existing methods for decoding text using beam search are expensive and require reinforcement learning. |
| Approach: | They propose a method that allows us to reap the full benefits of beam search with no additional computational cost. |
| Outcome: | The proposed method outperforms greedy decoding and beam search on machine translation tasks with minimal computational cost. |
Addressing Troublesome Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words. |
| Approach: | They propose to use contextual memory to memorize which target words should be produced in which situations to translate troublesome words. |
| Outcome: | The proposed method outperforms baseline models on Chinese-to-English and English-to German translation tasks. |
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
| Outcome: | The proposed model produces fluent translations with better reordering than previous models. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
Contextual Parameter Generation for Universal Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to multilingual neural machine translation lack language-specific parameterization. |
| Approach: | They propose a modification to existing neural machine translation models that allows for language specific parameterization and domain adaptation. |
| Outcome: | The proposed model surpasses state-of-the-art for both the IWSLT-15 and IWSTL-17 datasets and can perform zero-shot translation. |
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
| Approach: | They propose a method that adds synthetic data to sentences with high prediction loss during training and a variety of sampling strategies targeting difficult-to-predict words. |
| Outcome: | The proposed method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction (D18-1)
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| Challenge: | Existing methods for bilingual lexicon induction take advantage of word embeddings, but our model is not as efficient as previous work. |
| Approach: | They propose a discriminative latent-variable model for bilingual lexicon induction that combines the bipartite matching dictionary prior and an embedding-based approach. |
| Outcome: | The proposed model outperforms existing models on six language pairs and shows that it mitigates hubness problem. |
Non-Adversarial Unsupervised Word Translation (D18-1)
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| Challenge: | Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. |
| Approach: | They propose a method that aligns two words in two languages and iteratively refines the alignment. |
| Outcome: | The proposed method achieves better performance than state-of-the-art deep adversarial approaches on word translation of European and Non-European languages. |
Semi-Autoregressive Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to neural machine translation are typically autoregressive but suffer from low parallelizability and thus slow at decoding long sequences. |
| Approach: | They propose a semi-autoregressive Transformer model for fast sequence generation that keeps the autoregressive property in global but relieves in local . |
| Outcome: | The proposed model achieves 5.58 speedup while maintaining 88% translation quality, significantly better than previous non-autoregressive methods. |
Understanding Back-Translation at Scale (D18-1)
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| Challenge: | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. |
| Approach: | They propose to augment parallel training corpus with back-translations of target language sentences to improve neural machine translation with monolingual data. |
| Outcome: | The proposed method achieves a state-of-the-art of 35 BLEU on the WMT’14 English-German test set. |
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages (D18-1)
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| Challenge: | Existing approaches to transliteration generation require a large number of training examples. |
| Approach: | They propose a bootstrapping algorithm that uses constrained discovery to improve generation . they show that the model can be used with as few as 500 training examples . |
| Outcome: | The proposed method improves on nine languages written in a unique script. |
NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings (D18-1)
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| Challenge: | Existing methods for learning multilingual word embeddings assume that embeddable spaces of different languages exhibit similar structures. |
| Approach: | They propose a method for learning neighborhood sensitive maps to capture such differences . aim is to learn word vectors where similar words have similar vector representations . |
| Outcome: | The proposed method outperforms state-of-the-art methods for translation between distant languages. |
Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)
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| Challenge: | End-to-end neural machine translation (NMT) has attracted increasing attention in recent years. |
| Approach: | They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning. |
| Outcome: | The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism . |
Improving the Transformer Translation Model with Document-Level Context (D18-1)
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| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
| Outcome: | Experiments on NIST Chinese-English and IWSLT French-English datasets show that the proposed translation model outperforms the Transformer model significantly. |
MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)
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| Challenge: | Noisy input text can cause disastrous mistranslations in most modern machine translation systems. |
| Approach: | They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types. |
| Outcome: | The proposed dataset can provide an attractive testbed for noise-robust machine translation systems. |
SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach (D18-1)
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| Challenge: | ambiguity in the data bounds performance of the SimpleQuestions dataset, which is commonly used for factoid questions . ambiguities are a problem because many questions have more than one equally plausible interpretation . |
| Approach: | They propose a benchmark that can be solved by standard methods using the SimpleQuestions dataset . they propose ambiguity in the data bounds performance at 83.4% and a baseline that sets a new state-of-the-art performance level at 78.1% accuracy . |
| Outcome: | The SimpleQuestions dataset is one of the most commonly used benchmarks for studying factoids . the new benchmark is 78.1% accurate, and the upperbound is loose, the authors show . |
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (D18-1)
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| Challenge: | Existing QA models rely on learning interaction between document and question . current models require explicit attention to the document before or as it reads it . |
| Approach: | They propose a modular question answering task that enforces complete independence of the document encoder from the question encoder. |
| Outcome: | The proposed model achieves reasonable accuracy but significantly underperforms unconstrained QA models. |
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (D18-1)
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| Challenge: | Recent work has combined open-domain question answering with machine comprehension models to find answers in a large knowledge source. |
| Approach: | They propose a machine comprehension model that ranks paragraphs of retrieved documents for a higher answer recall with less noise. |
| Outcome: | The proposed model improves on four open-domain QA datasets by 7.8% on average. |
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Adaptive Document Retrieval for Deep Question Answering (D18-1)
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| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Why is unsupervised alignment of English embeddings from different algorithms so hard? (D18-1)
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| Challenge: | a new paper challenges word embedding algorithms to align independent English word embeds with 100% precision . authors show that when two different embeddables are used, they fail to do so . |
| Approach: | They propose to use unsupervised bilingual dictionary induction to study English-English alignments. |
| Outcome: | The proposed approach is more of a challenge than a technical contribution . it shows that the results challenge unsupervised bilingual dictionary induction algorithms . |
Quantifying Context Overlap for Training Word Embeddings (D18-1)
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| Challenge: | Experimental results show that word embeddings can be improved using word embeds . word embedings are a popular form of natural language processing . |
| Approach: | They propose to estimate second order co-occurrence relations based on context overlap . they use the augmented data to enhance word embeddings learning . |
| Outcome: | The proposed model improves word vectors for word similarity and downstream NLP tasks. |
Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space (D18-1)
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| Challenge: | Existing approaches to capture semantic relations of words in vector space are lacking information on lexico-syntactic patterns that connect word pairs in a corpus. |
| Approach: | They propose a pattern-based approach that exploits lexico-syntactic patterns as word pairs . they propose NLRA to generalize co-occurrences of word pairs and lexicon-sensitized embeddings of the word pairs that do not co-occur. |
| Outcome: | The proposed model outperforms existing models on measuring relational similarity . it can generalize word pairs and lexico-syntactic patterns and obtain embeddings of word pairs that do not co-occur . |
Generalizing Word Embeddings using Bag of Subwords (D18-1)
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| Challenge: | Existing word embeddings techniques have a fixed vocabulary, i.e., they can only provide vectors over a finite set of common words that appear frequently in a given corpus. |
| Approach: | They propose a subword-level word vector generation model that views words as bags of character n-grams and provides good vectors for rare or unseen words. |
| Outcome: | The proposed model performs state-of-the-art in English word similarity task and in joint prediction of part-of speech tag and morphosyntactic attributes in 23 languages. |
Neural Metaphor Detection in Context (D18-1)
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| Challenge: | Existing models focus on limited forms of linguistic context, such as unigrams. |
| Approach: | They propose end-to-end neural models for detecting metaphorical word use in context . they show that bi-directional biLSTM models which operate on complete sentences work well . |
| Outcome: | The proposed models show that they can learn rich contextual word representations . they are compared to previous models which focused on limited linguistic context . |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Unsupervised Bilingual Lexicon Induction via Latent Variable Models (D18-1)
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| Challenge: | Existing methods for generating bilingual lexicons use parallel corpora or bilingual dictionaries. |
| Approach: | They propose a model which builds cross-lingual dictionaries using latent variable models and adversarial training with no parallel corpora. |
| Outcome: | The proposed model outperforms state-of-the-art models on several language pairs and reaches competitive performance. |
Learning Unsupervised Word Translations Without Adversaries (D18-1)
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| Challenge: | Current methods for word translation are based on adversarial models and suffer from instability and hyper-parameter sensitivity. |
| Approach: | They propose a statistical dependency-based approach to bilingual dictionary induction that is unsupervised and introduces no adversary. |
| Outcome: | The proposed method outperforms adversarial alternatives and is much easier to train. |
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)
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| Challenge: | In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. |
| Approach: | They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks. |
| Outcome: | The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks. |
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning (D18-1)
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| Challenge: | Existing attention mechanisms are hard and hard, but they are more accurate when trained. |
| Approach: | They propose to use a beam approximation of the joint distribution between attention and output to train sequence to sequence learning. |
| Outcome: | The proposed method is compared to existing attention mechanisms on five translation tasks and shows consistent gains on the same tasks. |
Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
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| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
| Outcome: | The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs. |
Exploring Optimism and Pessimism in Twitter Using Deep Learning (D18-1)
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| Challenge: | Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need it. |
| Approach: | They propose deep learning models to predict optimism and pessimism in Twitter . they also show that a sentiment classifier would not be sufficient for predicting optimism and psi . |
| Outcome: | The proposed models outperform traditional machine learning classifiers on optimism and pessimism in Twitter. |
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (D18-1)
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| Challenge: | Pre-trained word embeddings provide significant improvements over untrained embeddables . Feature analysis reveals structural patterns of headline popularity . |
| Approach: | They use a multi-task GRU network to model headline popularity . they find that pre-trained word embeddings provide significant improvements over untrained embeddables . |
| Outcome: | The proposed model improves on pre-trained word embeddings and untrained embeddables . it also improves with the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging . |
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates (D18-1)
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| Challenge: | Existing models for agreement/disagreement in debates lack the ability to model these two factors together. |
| Approach: | They propose a hybrid attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three (dis)agreement inference datasets. |
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information (D18-1)
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| Challenge: | a new method for text classification ignores strong non-linguistic similarities like homophily . authors are typically represented via their linguistic profiles, i.e. information avail-able in the text . |
| Approach: | They use homophily cues to retrofit text-based author representations with non-linguistic information and introduce a trade-off parameter. |
| Outcome: | The proposed method improves on two author-attribute prediction tasks with large labels. |
A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation (D18-1)
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| Challenge: | Existing neural language models generate generic responses with poor logic and no emotion. |
| Approach: | They propose a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation using pre-generated emotion keywords and topic keywords. |
| Outcome: | The proposed approach improves the diversity of responses and boosts logic and emotion compared with baselines. |
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)
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| Challenge: | Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage. |
| Approach: | They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. |
| Outcome: | The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |
Modeling Temporality of Human Intentions by Domain Adaptation (D18-1)
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| Challenge: | Recent research shows that themes and words within a conversation change across time, whereas topics and the patient's attitude towards their willingness to change might shift. |
| Approach: | They propose a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). |
| Outcome: | The proposed method improves on a college alcoholism dataset using a bi-LSTM and topic model to learn language usage change across different time sessions. |
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)
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| Challenge: | Experimental results show that our model can generate semantically coherent responses compared to baseline models. |
| Approach: | They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module . |
| Outcome: | Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models. |
A Dataset for Document Grounded Conversations (D18-1)
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| Challenge: | a dataset of document grounded conversations provides information on content of a document . current datasets lacking conversation grounding do not provide this information . |
| Approach: | They propose a document grounded dataset for conversations . they use Wikipedia articles about popular movies to define document grounded conversations based on their results . |
| Outcome: | The proposed dataset provides a source of information and provides benchmark performance on the task of generating the next response. |
Out-of-domain Detection based on Generative Adversarial Network (D18-1)
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| Challenge: | Existing methods for out-of-domain (OOD) detection require huge effort to collect OOD sentences. |
| Approach: | They propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. |
| Outcome: | The proposed method is most accurate compared to existing methods on multi-domain dialog systems. |
Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
| Challenge: | In argumentation domain, people are exposed directly to audio (or the video), without access to a written version. |
| Approach: | They present a task for machine listening comprehension in the argumentation domain and a dataset in English. |
| Outcome: | The proposed task is based on 200 speeches arguing for or against 50 controversial topics and uses baseline methods to address it. |
Using active learning to expand training data for implicit discourse relation recognition (D18-1)
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| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
| Approach: | They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives. |
| Outcome: | The proposed model expands the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. |
Learning To Split and Rephrase From Wikipedia Edit History (D18-1)
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| Challenge: | Performing split and rephrase tasks is one of the main operations in text simplification, alongside paraphrasing and dropping less salient content. |
| Approach: | They propose to use Wikipedia's edit history to extract a rich new dataset for the task. |
| Outcome: | The proposed model scores 32 BLEU points above the previous best on the WebSplit benchmark. |
BLEU is Not Suitable for the Evaluation of Text Simplification (D18-1)
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| Challenge: | BLEU is widely considered to be an informative metric for text-to-text generation . Xu et al. (2016) found that BLUE is not suitable for evaluation of sentence splitting . |
| Approach: | They propose to use BLEU to evaluate sentence splitting as a metric for machine translation . they propose to compare BLUE with a corpus containing multiple structural paraphrases . |
| Outcome: | The proposed BLEU is not suitable for evaluation of sentence splitting . a correlation analysis with human judgments shows low correlation with BLUE . |
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information (D18-1)
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| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
| Approach: | They propose to use dialogue corpus to generate relevant responses by using prototypes to extract semantic information from PMN. |
| Outcome: | The proposed model outperforms classical and strong baseline models in generating relevant and informative responses. |
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior (D18-1)
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| Challenge: | Recent research shows that Reinforcement Learning (RL) approaches suffer from the exposure bias problem. |
| Approach: | They propose a Reinforcement Learning (RL) based training framework that constrains the action space using an n-gram language prior. |
| Outcome: | The proposed model is more human readable and graceful. |
Training for Diversity in Image Paragraph Captioning (D18-1)
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| Challenge: | Existing image captioning models have a lack of diversity between sentences . current models have limited their effectiveness due to repetitive paragraphs . |
| Approach: | They propose to apply sequence-level training to image paragraph captioning models . they find that standard self-critical training produces poor results . |
| Outcome: | The proposed training improves on the Visual Genome dataset with no architectural changes. |
A Graph-theoretic Summary Evaluation for ROUGE (D18-1)
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| Challenge: | ROUGE is one of the most widely used evaluation metrics for text summarization. |
| Approach: | They propose to use ROUGE to evaluate summaries based on lexical and semantic similarities. |
| Outcome: | The proposed method improves ROUGE's correlation with human judgments by exploiting lexical and semantic similarities. |
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
| Outcome: | The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. |
Evaluating Multiple System Summary Lengths: A Case Study (D18-1)
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| Challenge: | Practical summarization systems are expected to produce summaries of varying lengths, per user needs. |
| Approach: | They propose to use ROUGE metric to evaluate system summaries of multiple lengths. |
| Outcome: | The evaluation protocol in question is competitive, the authors show . they found that the evaluation protocol is competitive with existing benchmarks. |
Neural Latent Extractive Document Summarization (D18-1)
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| Challenge: | Existing summarization paradigms focus on extractive summarizing based on sentence level labels . |
| Approach: | They propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. |
| Outcome: | The proposed model outperforms a strong extractive baseline trained on rule-based labels and performs competitively with several recent models. |
On the Abstractiveness of Neural Document Summarization (D18-1)
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (D18-1)
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| Challenge: | Existing systems for automatic essay scoring are trained to predict the score of each essay at a time without considering rating schema. |
| Approach: | They propose a reinforcement learning framework that incorporates quadratic weighted kappa as guidance to optimize the scoring system. |
| Outcome: | Experiments on benchmark datasets show the proposed framework is effective. |
Identifying Well-formed Natural Language Questions (D18-1)
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| Challenge: | Understanding search queries is a hard problem as it involves dealing with “word salad” text. |
| Approach: | They propose to identify a well-formed natural language question from a dataset of 25,100 questions. |
| Outcome: | The proposed classifier improves the performance of neural sequence-to-sequence models for generating questions for reading comprehension. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
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| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (D18-1)
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| Challenge: | Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm . |
| Approach: | They propose an interactive supervised topic model based on the anchor words algorithm . they propose a classifier which requires no training beyond topic inference . |
| Outcome: | The proposed model is human-interpretable and fast, and can be interactive. |
Coherence-Aware Neural Topic Modeling (D18-1)
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| Challenge: | Topic models are evaluated for their ability to describe documents well (i.e. low perplexity) topic coherence is not optimized for and is only evaluated after training. |
| Approach: | They propose to incorporate a topic coherence objective into the training process by incorporating a coherency objective into a model. |
| Outcome: | The proposed model exhibits similar level of perplexity as baseline models but significantly higher topic coherence. |
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models (D18-1)
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| Challenge: | Recent advances in text normalization have limited applications in other languages . a novel approach to text normalizing uses character embeddings and word embedds . |
| Approach: | They propose a sequence-to-sequence model with character-based attention that uses pre-trained word embeddings to model subword information. |
| Outcome: | The proposed model achieves state-of-the-art F1 score on Arabic spelling correction task despite being small and unsuited for the task. |
Topic Intrusion for Automatic Topic Model Evaluation (D18-1)
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| Challenge: | Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. |
| Approach: | They propose to use topic intrusion to guess an outlier topic given a document and a few topics to automate the task. |
| Outcome: | The proposed method improves upon the state-of-the-art method and shows it can be used as an alternative to topic perplexity evaluation. |
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents (D18-1)
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| Challenge: | a web text structure is underutilized, but its visual organization is useful for NLP tasks . a flexible system for extracting hierarchical section titles and prose organization is developed . |
| Approach: | a new system extracts hierarchical section titles and prose organization from web documents . the system uses features from syntax, semantics, discourse and markup to build two models . |
| Outcome: | a new system extracts the hierarchical section titles and prose organization of web documents . the system achieves an overall precision of 0.82 and a recall of 0.98 on three domains of web text . |
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation (D18-1)
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| Challenge: | Existing methods for data augmentation for text-based tasks such as machine translation are limited due to noise and noise. |
| Approach: | They propose a data augmentation policy with desirable properties as an optimization problem and propose 'SwitchOut' switchout randomly replaces words in both the source and target sentences with other random words from their corresponding vocabularies. |
| Outcome: | The proposed method outperforms strong alternatives such as word dropout on three translation datasets. |
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder (D18-1)
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| Challenge: | Unsupervised learning of cross-lingual word embeddings has fundamental limitations in translating sentences. |
| Approach: | They propose a method to improve word-by-word translation of cross-lingual embeddings using monolingual corpora without any back-translation. |
| Outcome: | The proposed system surpasses state-of-the-art unsupervised translation systems without costly iterative training. |
Decipherment of Substitution Ciphers with Neural Language Models (D18-1)
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| Challenge: | Existing methods for deciphering homophonic substitution ciphers use pre-trained neural LMs. |
| Approach: | They propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. |
| Outcome: | The proposed beam search algorithm improves on challenging ciphers with smaller beam sizes and better error rates than state-of-the-art methods. |
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)
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| Challenge: | Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data. |
| Approach: | They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL . |
| Outcome: | The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings. |
Compact Personalized Models for Neural Machine Translation (D18-1)
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| Challenge: | a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality. |
| Approach: | They propose gradient-based domain adaptation methods for self-attentive machine translation models . they encourage structured sparsity in the set of offset tensors during learning . |
| Outcome: | The proposed method achieves high space and time efficiency using sparse models . the results compare the proposed method with incremental adaptation . |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
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| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
Supervised Domain Enablement Attention for Personalized Domain Classification (D18-1)
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| Challenge: | Recent IPDAs cover more than several thousands of diverse domains including Alexa Skills, Google Actions, and Cortana Skills. |
| Approach: | They propose a supervised enablement attention mechanism that utilizes sigmoid activation for the attention weighting and self-distillation to leverage the attention information of other enabled domains. |
| Outcome: | The proposed approach improves domain classification performance on real-world domains. |
A Deep Neural Network Sentence Level Classification Method with Context Information (D18-1)
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| Challenge: | Existing methods that use context for sentence classification are difficult to scale . Usually, sentences are treated as separate instances for the task . however, in many situations the sentence that is the focus of classification appears in a context that can provide additional information. |
| Approach: | They propose a method that uses potentially large contexts to classify sentences . they use an LSTM, and short-span features to classize sentences based on a stacked CNN . |
| Outcome: | The proposed method consistently improves on two different datasets. |
Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)
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| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
| Approach: | They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor. |
| Outcome: | The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability. |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Retrieval-Based Neural Code Generation (D18-1)
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Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig
| Challenge: | Existing methods to generate program source code from natural language are not able to generate complex code due to a lack of ability to memorize large and complex structures. |
| Approach: | They propose a method that uses subtree retrieval to explicitly reference existing code examples within a neural code generation model. |
| Outcome: | The proposed method improves performance on two code generation tasks by up to +2.6 BLEU. |
SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query. |
| Approach: | They propose a graph-to-sequence model to encode global structure information into node embeddings. |
| Outcome: | The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets. |
Generating Syntactic Paraphrases (D18-1)
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| Challenge: | Using data-to-text generation, text-totext generation and text reduction, we show that conditioning text generation on syntactic constraints permits the generation of syntakically distinct paraphrases for the same input. |
| Approach: | They propose to use four different models for automatic generation of syntactic paraphrases to study the automatic generation process. |
| Outcome: | The proposed models can generate syntactic paraphrases for the same input and exploit different types of input to increase the number of distinct paraphrased for a given input. |
Neural-Davidsonian Semantic Proto-role Labeling (D18-1)
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| Challenge: | Existing models for semantic proto-role labeling are based on a bidirectional LSTM encoding strategy. |
| Approach: | They propose a neural model for semantic proto-role labeling using a bidirectional LSTM encoding strategy that is adapted for the task. |
| Outcome: | The proposed model achieves state-of-the-art in a sentence with a LSTM encoder and a decoder. |
Conversational Decision-Making Model for Predicting the King’s Decision in the Annals of the Joseon Dynasty (D18-1)
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| Challenge: | Social psychologists note that decision making affects the performance of the group and that leadership plays a role. |
| Approach: | They propose a hierarchical RNN model with attention and pre-trained speakers to predict leaders’ decisions from the data. |
| Outcome: | The proposed model outperforms baselines to predict leaders’ decisions from the data. |
Toward Fast and Accurate Neural Discourse Segmentation (D18-1)
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| Challenge: | Existing discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. |
| Approach: | They propose an end-to-end neural segmenter based on BiLSTM-CRF framework that can segment texts fast and accurately using a large corpus. |
| Outcome: | The proposed model is significantly faster than previous methods while achieving state-of-the-art performance on the RST-DT corpus. |
A Dataset for Telling the Stories of Social Media Videos (D18-1)
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| Challenge: | Existing datasets focused on pre-selected human activities, whereas social media videos contain a great diversity of topics. |
| Approach: | They propose a large-scale dataset for video description as a new challenge for multi-sentence video description. |
| Outcome: | The proposed dataset contains 20k videos with 123k sentences, temporally aligned to the video. |
Cascaded Mutual Modulation for Visual Reasoning (D18-1)
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| Challenge: | Visual reasoning is a multi-step and compositional problem that requires intensive text-vision interactions. |
| Approach: | They propose a visual reasoning model that uses a feature-wise linear modulation technique to enable textual/visual pipelines to mutually control each other. |
| Outcome: | The proposed model outperforms existing models on visual reasoning benchmarks CLEVR and NLVR . it can generate a textual answer to a visual question answering problem with images . |
How agents see things: On visual representations in an emergent language game (D18-1)
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| Challenge: | Existing studies focus on the agents’ symbol usage, rather than on their representation of visual input. |
| Approach: | They propose to use visual representations of objects to create language-like communication systems by integrating them with the visual input of a game. |
| Outcome: | The proposed model and setup of Lazaridou et al. (2017) show that the representations of the agents' symbols do not capture the conceptual properties of the objects depicted in the input images. |
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)
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| Challenge: | Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts. |
| Approach: | They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule. |
| Outcome: | The proposed method improves the precision of the predicted relations with different benchmarks. |
Put It Back: Entity Typing with Language Model Enhancement (D18-1)
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| Challenge: | Existing Entity typing models suffer from noisy labels due to distant supervision . |
| Approach: | They propose to enhance existing entity typing models with language model enhancement to measure compatibility between context sentences and labels. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on benchmark datasets and is available on github. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages (D18-1)
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| Challenge: | Using a hierarchical model, we aim to extract all the publication strings from a researcher's homepage. |
| Approach: | They propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF and an alternating training method for training the model. |
| Outcome: | The proposed model outperforms the state-of-the-art models by 11.8% in F1-score on real data. |
A Neural Transition-based Model for Nested Mention Recognition (D18-1)
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| Challenge: | Existing methods to recognize nested mentions are based on Stack-LSTM . nesting mentions can be used for downstream tasks like question answering and relation extraction. |
| Approach: | They propose a scalable transition-based method to model the nested structure of mentions. |
| Outcome: | The proposed method gets the state-of-the-art performance in ACE datasets showing its effectiveness in detecting nested mentions. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |
Effective Use of Context in Noisy Entity Linking (D18-1)
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| Challenge: | Effectively using an entity mention's context to disambiguate it is the crux of the entity linking task. |
| Approach: | They propose to use convolutional neural networks to extract cues from context to effectively disambiguate between closely related concepts. |
| Outcome: | The proposed model outperforms previous work on the WikilinksNED test set by 2.8% absolute. |
Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)
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| Challenge: | Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance . |
| Approach: | They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it . |
| Outcome: | The proposed model achieves best F1 score compared to the state-of-the-art models. |
Do explanations make VQA models more predictable to a human? (D18-1)
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| Challenge: | Existing explanations of a model's behavior are not used in interactive tasks like Visual Question Answering (VQA). |
| Approach: | They analyze existing explanations and their role in making a VQA model more predictable to a human by using human-in-the-loop approaches that treat the model as a black-box. |
| Outcome: | The proposed explanations make a model more predictable to humans, whereas human-in-the-loop approaches treat it as a black-box do. |
Facts That Matter (D18-1)
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| Challenge: | Existing methods to discover facts from natural language text are based on relation extraction and open information extraction. |
| Approach: | They propose a task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. |
| Outcome: | The proposed system outperforms baselines and text summarizers in a supervised evaluation of salience tasks. |
Entity Tracking Improves Cloze-style Reading Comprehension (D18-1)
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| Challenge: | Recent work on reading comprehension tasks has improved with simple approaches, but still trail human performance. |
| Approach: | They propose to add additional entity features and a multi-task tracking objective to improve model performance . they compare the model's predictions with those of more complicated models . |
| Outcome: | The proposed model outperforms the current state of the art on the LAMBADA dataset by 8 pts. |
Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)
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| Challenge: | Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer. |
| Approach: | They propose to use domain adaptation to detect duplicate questions in forums . they find that domain adaptation improves performance over multiple pairs of domains . |
| Outcome: | The proposed approach improves 5.6% over the best baseline across multiple pairs of domains. |
Translating a Math Word Problem to a Expression Tree (D18-1)
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| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection (D18-1)
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| Challenge: | Recent NLP approaches that model relations between text use complex architectures and attention. |
| Approach: | They propose to use labelled data to model semantic relations between two pieces of text . they use word representations to encode matching features directly in the word representation . |
| Outcome: | The proposed approach beats tree kernel models and neural models with similar input encodings while keeping the model simple and fast to train. |
A dataset and baselines for sequential open-domain question answering (D18-1)
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| Challenge: | Existing question-answering systems focus on answering individual questions, assuming they are devoid of context. |
| Approach: | They propose to ask multiple related questions in a dataset that includes human-authored questions. |
| Outcome: | The proposed system can answer human-authored questions better than existing systems. |
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set (D18-1)
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| Challenge: | Recent studies have demonstrated remarkable performance in text classification tasks such as Arabic dialect identification. |
| Approach: | They propose two approaches to improve string kernels' accuracy in Arabic and English . first approach interprets pairwise string kernel similarities between training and test sets as features . second approach adapts to training set and adds test samples for another round of training . |
| Outcome: | The proposed methods improve English polarity classification and Arabic dialect identification. |
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification (D18-1)
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| Challenge: | Existing approaches to aspect level sentiment classification ignore aspect information, causing large error. |
| Approach: | They propose a parameterized convolutional neural network for aspect level sentiment classification . they incorporate aspect information into convolutionally-based neural networks . |
| Outcome: | The proposed model achieves excellent results on SemEval 2014 datasets. |
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network (D18-1)
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| Challenge: | Existing approaches to emotion detection are lexicon-based, graphical model-based and linear classifier-based. |
| Approach: | They propose a transfer learning architecture to divide sentence representation into two different feature spaces which capture general sentiment words and other important emotion-specific words via a dual attention mechanism. |
| Outcome: | The proposed model can capture general sentiment words and other emotion-specific words via a dual attention mechanism on two benchmark datasets. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
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| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
| Outcome: | The proposed model outperforms pipeline approaches in aspects-based sentiment analysis. |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
Syntactical Analysis of the Weaknesses of Sentiment Analyzers (D18-1)
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| Challenge: | polarity items are lexical items that can only appear in specific licensing contexts. |
| Approach: | They analyze sentiment analyzers for negative polarity items and a set of 150 test sentences . they describe a syntactic phenomenon that an ideal sentiment analyzeur must understand . |
| Outcome: | The proposed method focuses on two sentential structures: downward entailment and non-monotone quantifiers. |
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender (D18-1)
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| Challenge: | Existing research suggests that the sound of a person’s first name is associated with the person’ s gender, but no research has attempted to assess the gender of brand names. |
| Approach: | They propose a machine-learning method that uses sound symbolism to assess the gender of brand names. |
| Outcome: | The proposed method can predict gender of human first names with high accuracy . it uses linguistic features of name endings to predict gender . |
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (D18-1)
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| Challenge: | Existing pipelines for fact-checking of textual sources are limited . fact- checking of text sources requires a large knowledge base to extract relevant information . |
| Approach: | They propose a neural ranker that dynamically selects sentences to improve evidence retrieval . they incorporate lexical tagging methods into the pipeline framework to simplify the tasks . |
| Outcome: | The proposed model outperforms the existing TF-IDF method on a large-scale fact extraction and verification dataset with speedup. |
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
| Outcome: | The proposed model outperforms baseline models which use popularity as informativeness measure. |
Identifying Locus of Control in Social Media Language (D18-1)
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| Challenge: | lexical features outperform syntactic features in expressing control in social media . authors communicate internal locus of control when they ascribe control to themselves . |
| Approach: | They examine the role of syntax and semantics in expressing users’ sense of control in annotated Facebook posts. |
| Outcome: | The proposed language outperforms syntactic features in identifying whether or not a user is in control of their circumstances. |
Somm: Into the Model (D18-1)
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| Challenge: | sommeliers have three skills: wine theory, blind tasting, and beverage service . current study suggests that the sophist profession is at least to some extent automatable . |
| Approach: | They propose to train machine learning models that match sommelier's skills and compare results with real data. |
| Outcome: | The proposed models outperform human sommeliers on most tasks, compared with real data from a large group of wine professionals. |
Fine-Grained Emotion Detection in Health-Related Online Posts (D18-1)
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| Challenge: | Emotion detection from health-related posts is based on a health-specific vocabulary that people use in OHCs. |
| Approach: | They propose to use deep neural networks and lexicon-based features to detect emotions in health-related posts. |
| Outcome: | The proposed method uses high-level and abstract features derived from deep neural networks combined with lexicon-based features to detect emotions. |
The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (D18-1)
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Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, H. Andrew Schwartz
| Challenge: | Social media data is often aggregated without regard to users in the Twitter populations of each community. |
| Approach: | They propose to use Twitter language to build community-level models using Twitter language aggregated by users. |
| Outcome: | The proposed method improves on four county-level tasks spanning demographic, health, and psychological outcomes over the standard approach of aggregating all tweets. |
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (D18-1)
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| Challenge: | Despite its success, neural autoregressive modeling has its weakness in decoding, i.e., finding the most likely sequence. |
| Approach: | They propose a conditional non-autoregressive neural sequence model based on iterative refinement based upon latent variable models and conditional denoising autoencoders. |
| Outcome: | The proposed model significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart. |
Large Margin Neural Language Model (D18-1)
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| Challenge: | Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. |
| Approach: | They propose a large margin criterion for training neural language models by minimizing perplexity on grammatical sentences and propose enlarged margins for task-specific training. |
| Outcome: | The proposed method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation. |
Targeted Syntactic Evaluation of Language Models (D18-1)
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| Challenge: | Recent advances have led to an explosion of neural network-based LM architectures. |
| Approach: | They propose to supplement perplexity with a metric that assesses whether a language model can predict the grammatical sentence more accurately than an ungrammatically-based model. |
| Outcome: | The proposed model performed poorly on many of the constructions. |
Rational Recurrences (D18-1)
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| Challenge: | Recent studies show that neural models lack strong intuitions . recent studies show connections between convolutional neural networks and weighted finite state automata (WFSAs) |
| Approach: | They show that some recurrent neural networks share a connection to weighted finite state automata (WFSAs) they define rational recurrences as recursive hidden state update functions . they propose to use these functions to write forward calculations of a finite set of WFSA's . |
| Outcome: | The proposed model outperforms two baselines on language modeling and text classification. |
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (D18-1)
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| Challenge: | Existing efforts to train pre-trained language models have brought significant improvements to various NLP applications. |
| Approach: | They propose to compress bulky LMs while preserving useful information for a specific task. |
| Outcome: | The proposed method can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. |
Automatic Event Salience Identification (D18-1)
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| Challenge: | Existing models for analyzing salience of discourse units are inadequate . authors propose two saliency detection models based on discourse relations . |
| Approach: | They propose two salience detection models based on discourse relations that capture complex interactions between discourse units. |
| Outcome: | The proposed models outperform the strong frequency baseline and improve the feature based model by a large margin. |
Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
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| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
| Outcome: | The proposed method predicts start and end-points without intermediate step of prediction of temporal relations . it evades phase 2 because there are n 2 possible entity pairs in the extraction phase . |
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (D18-1)
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| Challenge: | Event extraction is of practical utility in natural language processing . it is common that multiple events exist in the same sentence, causing difficulties in extracting them . |
| Approach: | They propose a framework to jointly extract multiple event triggers and arguments . they introduce syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
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| Challenge: | Distantly-supervised Relation Extraction (RE) methods ignore readily available side information. |
| Approach: | They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor. |
| Outcome: | The proposed method improves performance even when limited side information is available. |
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (D18-1)
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| Challenge: | Existing approaches to ACE event detection treat multiple events in one sentence as independent ones and recognize them separately. |
| Approach: | They propose a hierarchical and bias tagging network framework to detect multiple events in one sentence collectively and a gated multi-level attention mechanism to automatically extract and fuse the sentence-level and document-level information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a 2005 ACE dataset. |
Valency-Augmented Dependency Parsing (D18-1)
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| Challenge: | valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents. |
| Approach: | They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them. |
| Outcome: | The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7. |
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections (D18-1)
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| Challenge: | Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. |
| Approach: | They propose a generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. |
| Outcome: | The proposed model outperforms state-of-the-art models on part-of speech (POS) induction and unsupervised dependency parsing without gold POS annotation. |
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing (D18-1)
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| Challenge: | Top-down and in-order shift-reduce constituent parsers are the most accurate known shift-reducing algorithms for constituent paring. |
| Approach: | They propose to use dynamic oracles to train two of the most accurate shift-reduce algorithms for constituent parsing. |
| Outcome: | The proposed top-down and in-order shift-reduce parsers improve on the WSJ benchmark. |
Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
| Outcome: | The proposed method outperforms the Vinyals et al. (2015) sequence-to-sequence parser by 90% on the PTB and CTB treebanks. |
Synthetic Data Made to Order: The Case of Parsing (D18-1)
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| Challenge: | supervised dependency parsing is a core task in natural language processing, but unsupervised parsers can hardly produce useful parses. |
| Approach: | They propose to permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. |
| Outcome: | The proposed method improves the parsing accuracy of a target language . the proposed method is based on a distribution of gold POS bigrams . |
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions (D18-1)
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| Challenge: | Existing approaches to visual question answering represent images using pre-trained CNNs . but they rarely provide any insight, apart from the answer, into the VQA process . |
| Approach: | They propose to break up the end-to-end VQA into two steps: explaining and reasoning . they first extract attributes and generate descriptions as explanations for an image . a reasoning module utilizes these explanations in place of the image to infer an answer . |
| Outcome: | The proposed system achieves comparable performance with baselines, but with added benefits of explanability and the ability to improve with higher quality explanations. |
Learning a Policy for Opportunistic Active Learning (D18-1)
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| Challenge: | Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in interactive object retrieval tasks. |
| Approach: | They propose to use active learning to constrain possible queries during interactions to improve grounding of natural language descriptions in an interactive object retrieval task. |
| Outcome: | The proposed policy trades off task completion with model improvement that would benefit future tasks while lowering the cost of annotation without sacrificing model performance. |
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes (D18-1)
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| Challenge: | Existing comprehension tests for QA are limited by the text sources and questionanswer formats. |
| Approach: | They propose a dataset for multimodal comprehension of cooking recipes . preliminary results indicate RecipeQA will serve as a challenging test bed . |
| Outcome: | The proposed dataset will serve as a test bed and ideal benchmark for evaluating machine comprehension systems. |
TVQA: Localized, Compositional Video Question Answering (D18-1)
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| Challenge: | Recent studies have focused on image-based question-answering (QA) tasks, but little has been done on video-based QA. |
| Approach: | They present a large-scale video QA dataset based on 6 popular TV shows . they provide analysis of the new dataset and trainable neural network framework . |
| Outcome: | The proposed dataset includes 152,545 QA pairs from 21,793 clips spanning over 460 hours of video. |
Localizing Moments in Video with Temporal Language (D18-1)
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| Challenge: | a novel model for localizing moments in a longer video using natural language queries is challenging . moment localization is similar to other language and vision tasks, but it offers an interesting opportunity to model temporal dependencies and reasoning in text. |
| Approach: | They propose a model that explicitly reasons about different temporal segments in a video . their dataset includes a dataset with real videos and template sentences . |
| Outcome: | The proposed model explicitly reasons about different temporal segments in a video . it shows that temporal context is important for localizing phrases which include temporal language . |
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models (D18-1)
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| Challenge: | Existing benchmarks for rare word representation are lacking for evaluation and comparison . a task-based evaluation does not provide a solid basis for comparing different models . |
| Approach: | They propose to use an expert-annotated word similarity dataset to evaluate rare word representation techniques. |
| Outcome: | The proposed dataset provides a reliable benchmark for rare word representation techniques. |
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)
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| Challenge: | Existing models for Word Sense Disambiguation use labeled data, but lack gloss knowledge. |
| Approach: | They propose a co-attention mechanism to generate co-dependent representations for context and gloss . they propose to incorporate gloss knowledge into neural networks for Word Sense Disambiguation . |
| Outcome: | The proposed model achieves state-of-the-art results on standard English all-words WSD datasets. |
Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations (D18-1)
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| Challenge: | a lack of datasets distinguish between conventionalized and novel metaphors is limiting research . a novel metaphor is often overlooked or intentionally disregarded, authors say . |
| Approach: | They propose a crowdsourced annotation layer for an existing metaphor corpus to investigate novelty . they investigate correlations between concreteness ratings and more semantic features . |
| Outcome: | The proposed method combines novel metaphor annotations with concreteness ratings and semantic features. |
Streaming word similarity mining on the cheap (D18-1)
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| Challenge: | Existing methods to estimate word similarities are to embed words in vector space and then calculate similarities between corresponding vectors. |
| Approach: | They propose a method that explicitly counts second-order co-occurrences to estimate word similarities from streams. |
| Outcome: | The proposed method is scalable, converges rapidly, behaves robustly under parameter changes, and captures word similarities on par with state-of-the-art word embeddings. |
Memory, Show the Way: Memory Based Few Shot Word Representation Learning (D18-1)
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| Challenge: | Existing word embedding methods for distributed semantic models require limited examples to learn a high quality representation. |
| Approach: | They propose a memory-based embedding learning method capable of acquiring word representations from limited context. |
| Outcome: | The proposed method delivers impressive performance on two challenging few-shot word similarity tasks. |
Disambiguated skip-gram model (D18-1)
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| Challenge: | Disambiguated skip-gram is a neural-probabilistic model for learning multi-sense word embeddings. |
| Approach: | They propose a model that is end-to-end differentiable and can be interpreted as a feed-forward neural network. |
| Outcome: | The proposed model improves state-of-the-art in word sense induction benchmarks. |
Picking Apart Story Salads (D18-1)
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| Challenge: | Story salads are mixtures of multiple documents that can be generated at scale . they exhibit challenging inference problems, and require global context and coherence . |
| Approach: | They propose to generate salads that exhibit challenging inference problems by exploiting the Wikipedia hierarchy . they propose a task where the objective is to group sentences from the same narratives . |
| Outcome: | The proposed task is based on a novel, challenging clustering task using Wikipedia . it is difficult to identify relevant information and assemble it into coherent narratives . |
Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (D18-1)
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| Challenge: | Existing approaches combine language and perception to infer word embeddings . however, the embeddables produced by such models do not reflect the actual word representations. |
| Approach: | They propose a probabilistic model that integrates linguistic and perceptual inputs to explain observed word-context pairs in a text corpus. |
| Outcome: | The proposed model achieves competitive or stronger results on tasks of assessing pairwise word similarity and image/caption retrieval compared to other state-of-the-art models. |
Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (D18-1)
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| Challenge: | In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem. |
| Approach: | They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task. |
| Outcome: | The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations. |
Dissecting Contextual Word Embeddings: Architecture and Representation (D18-1)
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| Challenge: | Existing work on learning contextual representations has used LSTM-based biLMs, but there is no reason to believe this is effective. |
| Approach: | They propose to use pre-trained bidirectional language models to learn contextual word embeddings for four NLP tasks and to use them to study the effects of architecture on endtask accuracy. |
| Outcome: | The proposed models outperform word embeddings for four NLP tasks and all learn representations that vary with network depth. |
Preposition Sense Disambiguation and Representation (D18-1)
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| Challenge: | Prepositions are highly polysemous and their variegated senses encode significant semantic information. |
| Approach: | They match each preposition’s context and their interplay to the geometry of the word vectors to the left and right of the preposition. |
| Outcome: | The proposed algorithm is comparable to and better than state-of-the-art on two benchmark datasets. |
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings (D18-1)
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| Challenge: | Monolingual dictionaries are widespread and semantically rich resources. |
| Approach: | They propose a model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. |
| Outcome: | The proposed model shows strong performance when trained exclusively on dictionary data and generalizes in one shot. |
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources (D18-1)
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| Challenge: | Existing word embeddings assign only one vector to each word, resulting in word disambiguation on smaller scales. |
| Approach: | They propose a task which aims to test different properties of word representations. |
| Outcome: | The proposed task is intuitive enough to annotate on a large scale while teasing out properties of popular lexical resources. |
Neural Multitask Learning for Simile Recognition (D18-1)
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| Challenge: | Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. |
| Approach: | They propose a neural network framework for simile sentence classification, simile component extraction and language modeling. |
| Outcome: | The proposed framework outperforms rule-based and feature-based approaches in simile sentence classification and simile component extraction tasks. |
Structured Alignment Networks for Matching Sentences (D18-1)
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| Challenge: | Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. |
| Approach: | They propose a model of structured alignments between sentences to compare two sentences by matching their latent structures. |
| Outcome: | The proposed model is differentiable and trained only on the matching objective. |
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference (D18-1)
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| Challenge: | Using a new architecture, alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Approach: | They propose a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Outcome: | The proposed architecture achieves competitive performance on three popular benchmarks, SNLI, MultiNLI and SciTail, while maintaining lightweight parameter size. |
Convolutional Interaction Network for Natural Language Inference (D18-1)
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| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Lessons from Natural Language Inference in the Clinical Domain (D18-1)
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| Challenge: | State of the art models with deep neural networks lack generalization capabilities in specialized domains where training data is limited. |
| Approach: | They propose a dataset annotated by doctors performing a natural language inference task grounded in the medical history of patients. |
| Outcome: | The proposed model outperforms existing models in the clinical domain by incorporating domain knowledge from external data and lexical sources. |
Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| Challenge: | Using question generation, we learn a semantic parser with 30% of the supervised training data. |
| Approach: | They propose to use question generation to learn a semantic parser with less supervised training data. |
| Outcome: | The proposed method improves the state-of-the-art model with less training data. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
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| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)
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| Challenge: | Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain. |
| Approach: | They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference . |
| Outcome: | The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples. |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |
Mapping Language to Code in Programmatic Context (D18-1)
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| Challenge: | Existing approaches for automatically mapping natural language to executable code have considered limited language or code environments. |
| Approach: | They propose a task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. |
| Outcome: | The proposed model can generate member functions from documentation and the class environment. |
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)
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| Challenge: | Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries. |
| Approach: | They propose a syntax tree network to address the complex text-to-SQL generation task. |
| Outcome: | The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus. |
Cross-lingual Decompositional Semantic Parsing (D18-1)
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| Challenge: | Renewed interest in semantic analysis has led to a surge of proposed new frameworks . many of these efforts are limited to the analysis of English, but with a number of exceptions e.g., recent efforts in Minimal Recursion Semantics (MRS) and multilingual FrameNet annotation and parsing. |
| Approach: | They propose a cross-lingual decompositional semantic analysis task based on a target language . they propose 'end-to-end' model with an annotating mechanism that supports intra-sentential coreference . |
| Outcome: | The proposed model outperforms baselines by at least 1.75 F1 score on an evaluation dataset. |
Learning to Learn Semantic Parsers from Natural Language Supervision (D18-1)
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| Challenge: | Existing logical forms require a user to be familiar with the underlying structure to learn a semantic parser. |
| Approach: | They propose a method for training semantic parsers from natural language feedback . they use natural language inputs to parse feedback to leverage it as a form of supervision . |
| Outcome: | The proposed algorithm learns a semantic parser from users’ corrections expressed in natural language. |
DeepCx: A transition-based approach for shallow semantic parsing with complex constructional triggers (D18-1)
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| Challenge: | Using DeepCx, we extend Shallow semantic parsing to include complex constructions . multi-word expressions and complex constructional arguments can express relational meanings - but little work has addressed tagging of such constructional triggers. |
| Approach: | They propose a neural-based surface construction labeling task that extends Shallow Semantic Parsing to include frames triggered by complex constructions. |
| Outcome: | The proposed system improves on the task of tagging causal language in English. |
What It Takes to Achieve 100% Condition Accuracy on WikiSQL (D18-1)
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| Challenge: | despite of its simplicity, none of the publicly reported structured query generation models can achieve an accuracy beyond 62%, which is far from enough for practical use. |
| Approach: | They propose a model that can achieve 88.6% condition accuracy on WikiSQL . they ask: why is the accuracy still low for such simple queries? |
| Outcome: | The proposed solution can reach up to 88.6% condition accuracy on the WikiSQL dataset. |
Better Transition-Based AMR Parsing with a Refined Search Space (D18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships. |
| Approach: | They propose to use a transition-based search space to conduct a new compact AMR graph and an improved oracle to perform the search. |
| Outcome: | The proposed system achieves the state-of-the-art performance on various datasets with minimal additional information. |
Heuristically Informed Unsupervised Idiom Usage Recognition (D18-1)
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| Challenge: | Existing models for idiom usage recognition have failed to recognize usages without annotated examples. |
| Approach: | They propose an unsupervised method for recognizing the intended usages of idioms by using distributional semantics to identify literal usages. |
| Outcome: | The proposed method performs competitively against supervised methods. |
Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research (D18-1)
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| Challenge: | a prior art claim that sense-specific vectors provide an advantage over normal vectors is unfounded in two ways. |
| Approach: | They claim that sense-specific vectors provide an advantage over normal vectors due to the polysemy that they presumably represent. |
| Outcome: | The proposed results show that ground-truth polysemy degrades performance in word similarity tasks and that random assignment of words to senses improves performance. |
Predicting Semantic Relations using Global Graph Properties (D18-1)
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| Challenge: | Semantic graphs encode the structural qualities of language as a representation of human knowledge. |
| Approach: | They propose a global-theoretic model that integrates global and local properties of semantic graphs to improve local prediction of relational relations between synsets. |
| Outcome: | The proposed model improves on the local task of predicting semantic relations between synsets, yielding state-of-the-art results on the WN18RR dataset. |
Learning Scalar Adjective Intensity from Paraphrases (D18-1)
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| Challenge: | Existing lexical resources do not include the relative intensities of adjectives. |
| Approach: | They propose a method to automatically learn relative intensity relation between scalar adjectives . they use a paraphrase-based method that assumes that a pair of adjectives is "really hot" a similar method is used to infer the polarity of indirect answers to "yes/no" questions . |
| Outcome: | The proposed method improves the quality of systems for ordering sets of scalar adjectives and inferring the polarity of indirect answers to "yes/no" questions. |
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions (D18-1)
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| Challenge: | Empirically, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. |
| Approach: | They propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions with a very short learning time. |
| Outcome: | The proposed measure can be applied to sparse linguistic expressions with a very short learning time, and is called the pointwise HSIC. |
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism (D18-1)
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| Challenge: | Existing approaches to automatic related work summarization rely on human-engineered features. |
| Approach: | They propose a neural data-driven attention mechanism to measure contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. |
| Outcome: | The proposed approach achieves significant improvement over a typical seq2seq summarization baseline and five classical summarizing baselines. |
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling (D18-1)
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| Challenge: | Existing neural abstractive methods for document summarization are not effective for document summary. |
| Approach: | They propose to extend basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. |
| Outcome: | The proposed model outperforms state-of-the-art methods on document summarization tasks significantly. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Improving Abstraction in Text Summarization (D18-1)
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| Challenge: | Abstractive text summarization models do not capture the abstractive nature of high quality summaries. |
| Approach: | They propose to decompose a decoder into a contextual network and a pretrained language model that incorporates prior knowledge about language generation. |
| Outcome: | The proposed model achieves comparable results to state-of-the-art models, based on ROUGE scores and human evaluations, while producing a significantly higher level of abstraction. |
Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment (D18-1)
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| Challenge: | Existing approaches to network embeddings focus on one-hot representations of vertices, which are not able to capture relationships between verti- ces. |
| Approach: | They propose to integrate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding methods on three real-world benchmarks for downstream tasks including link prediction and multi-label vertex classification. |
Learning Context-Sensitive Convolutional Filters for Text Processing (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) are a popular building block for natural language processing . despite their success, most existing CNN models share the same learned set of filters for all input sentences. |
| Approach: | They propose to use a meta network to learn context-sensitive convolutional filters for text processing by using a bidirectional filter generation mechanism. |
| Outcome: | The proposed framework outperforms standard and attention-based CNN models on four different tasks. |
Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
Learning Neural Representation for CLIR with Adversarial Framework (D18-1)
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| Challenge: | Existing studies in cross-language information retrieval (CLIR) use general text representation models that are not optimized for the target task. |
| Approach: | They propose a novel text representation model based on adversarial learning which seeks a task-specific embedding space for CLIR. |
| Outcome: | The proposed model outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline. |
AD3: Attentive Deep Document Dater (D18-1)
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| Challenge: | Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available. |
| Approach: | They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. |
| Outcome: | The proposed system outperforms neural and non-neural baselines on multiple real-world datasets. |
Gromov-Wasserstein Alignment of Word Embedding Spaces (D18-1)
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| Challenge: | Current unsupervised methods for learning cross-lingual correspondences involve multiple steps, including heuristic post-hoc refinement strategies. |
| Approach: | They propose to cast the correspondence problem directly as an optimal transport problem, building on the idea that word embeddings arise from metric recovery algorithms. |
| Outcome: | The proposed method can be estimated efficiently, requires little or no tuning, and performs comparable with the state-of-the-art in various unsupervised word translation tasks. |
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision (D18-1)
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| Challenge: | Indirect supervision is a promising direction to address the annotation bottleneck . end-to-end modeling with probabilistic logic is often intractable due to inference and learning . |
| Approach: | They propose a framework for indirect supervision that integrates deep learning with deep learning by combining probabilistic logic with deep-learning. |
| Outcome: | Experiments on biomedical machine reading demonstrate the potential of this framework. |
Deriving Machine Attention from Human Rationales (D18-1)
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| Challenge: | Attention-based models are successful when trained on large amounts of data. |
| Approach: | They propose an approach to map human-annotated rationales to high-performing attention and use this to guide models trained in low-resource scenarios. |
| Outcome: | The proposed model yields over 15% error reduction on benchmark datasets. |
Semi-Supervised Sequence Modeling with Cross-View Training (D18-1)
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| Challenge: | Unsupervised representation learning algorithms such as word2vec and ELMo only learn from task-specific labeled data during the main training phase. |
| Approach: | They propose a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
| Outcome: | The proposed algorithm improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. |
A Probabilistic Annotation Model for Crowdsourcing Coreference (D18-1)
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| Challenge: | Existing methods to generate annotated corpora for coreference are expensive and limited. |
| Approach: | They propose a model of annotation for aggregating crowdsourced anaphoric annotations. |
| Outcome: | The proposed model can extract from crowdsourced annotations coreference chains comparable to those obtained with expert annotation. |
A Deterministic Algorithm for Bridging Anaphora Resolution (D18-1)
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| Challenge: | Existing methods for bridging anaphora resolution only consider NPs’ head nouns and thus do not capture the semantics of NP. |
| Approach: | They propose a deterministic approach to bridging anaphora resolution which represents the semantics of an NP based on its head noun and modifications. |
| Outcome: | The proposed approach achieves competitive results compared to the best system in Hou et al. (2013) which explores Markov Logic Networks to model the problem. |
A Knowledge Hunting Framework for Common Sense Reasoning (D18-1)
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| Challenge: | a new system that uses common sense to solve a common sense problem is developed . a winograd schema challenge and a choice of plausible alternatives are popular tests . |
| Approach: | They propose an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge . they use a knowledge hunting module to gather web text for problem resolutions . |
| Outcome: | The proposed system achieves state-of-the-art on the Winograd Schema Challenge . it improves F1 performance on the full WSC by 0.21 over the previous best . |
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs (D18-1)
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| Challenge: | a paper addresses the problem of mapping natural language text to knowledge base entities. |
| Approach: | They propose a model for mapping natural language text to knowledge base entities using a multi-dimensional entity space obtained from a knowledge graph. |
| Outcome: | The proposed model is applied to large-scale text-to-entity mapping and entity classification tasks with state-of-the-art results. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
One-Shot Relational Learning for Knowledge Graphs (D18-1)
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| Challenge: | Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training. |
| Approach: | They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures. |
| Outcome: | The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations. |
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
| Approach: | They propose a temporally aware KG embedding method which incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. |
| Outcome: | The proposed method performs KG inference using temporal guidance and predicts scopes for relational facts with missing time annotations. |
Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
Entity Linking within a Social Media Platform: A Case Study on Yelp (D18-1)
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| Challenge: | Existing studies on entity linking focus on linking entities to knowledge bases, but on social media platforms, such as Yelp, it can be more practical. |
| Approach: | They propose to link entities within a social media platform with a new entity linking problem. |
| Outcome: | The proposed model can link business mentions to corresponding businesses on a social media platform. |
Annotation of a Large Clinical Entity Corpus (D18-1)
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| Challenge: | Past researches have shown the superiority of statistical/ML approaches over the rule based approaches. |
| Approach: | They propose to annotate a clinical domain annotated corpus using a small data set or a narrower domain to take full advantage of machine learning. |
| Outcome: | The proposed corpus contains 5,160 clinical documents from forty different clinical specialties. |
Visual Supervision in Bootstrapped Information Extraction (D18-1)
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| Challenge: | a list-based interface populated with informative samples is effective for data annotation . a 2D scatterplot populated by diverse and representative samples yields improved models . |
| Approach: | They propose a list-based interface that can be used to build efficient and effective data annotation models. |
| Outcome: | The proposed model learns the distributional similarity of entities through the patterns that match them in a large corpus while being discriminative with respect to human-labeled and machine-promoted entities. |
Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
| Approach: | They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision. |
| Outcome: | The proposed model can handle tokens with multiple possible labels under the traditional framework and improves on the existing model with a new Tie or Break scheme. |
Zero-Shot Open Entity Typing as Type-Compatible Grounding (D18-1)
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| Challenge: | Existing approaches to entity typing have limited flexibility to transfer across text genres and generalize to new type taxonomies. |
| Approach: | They propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. |
| Outcome: | The proposed system outperforms state-of-the-art supervised NER systems on a broad range of datasets and on 'biological domain' it is competitive with supervised systems and outperformed on out-of training datasets. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
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| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Interpretation of Natural Language Rules in Conversational Machine Reading (D18-1)
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Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel
| Challenge: | Existing work on question answering problems requires the reading of text because it contains a recipe to derive an answer together with the reader’s background knowledge. |
| Approach: | They formalise a task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. |
| Outcome: | The proposed task is based on 37k task instances based in real-world rules and crowd-generated questions and scenarios. |
A State-transition Framework to Answer Complex Questions over Knowledge Base (D18-1)
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| Challenge: | Existing methods for complex question answering have some limitations . existing methods employ predefined patterns or templates to understand complex questions. |
| Approach: | They propose a state transition-based approach to translate a natural language question to a semantic query graph. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on several benchmarks with two knowledge bases. |
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)
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| Challenge: | Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources. |
| Approach: | They propose a multi-answer multi-task framework that uses multiple reference answers for multiple questions. |
| Outcome: | The proposed model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09 . |
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (D18-1)
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| Challenge: | Experimental results reveal dual structure between OIE and OIN tasks helps to build better OIE agents and OINE agents. |
| Approach: | They propose an Open-Domain Information Narration task as the reverse task of Open Information Extraction (OIE) they then propose an OIN task as an OIE agent and an OIR agent to implement the dual structure . |
| Outcome: | The proposed task is the reverse task of Open Information Extraction (OIE) The proposed system is able to implement the dual structure with a reinforcement learning paradigm. |
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller (D18-1)
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| Challenge: | Existing approaches to machine reading comprehension are limited in understanding, up to a few paragraphs, failing to comprehend lengthy documents. |
| Approach: | They propose a deep neural network architecture to handle a long-range dependency in RC tasks. |
| Outcome: | The proposed method outperforms existing methods especially for lengthy documents. |
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension (D18-1)
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| Challenge: | Sequence encoders are crucial components in many neural architectures for learning to read and comprehend. |
| Approach: | They propose a compositional encoder that explicitly models across multiple granularities using a new dilated composition mechanism. |
| Outcome: | The proposed encoder is fast and expressive, and can model across multiple granularities. |
Neural Compositional Denotational Semantics for Question Answering (D18-1)
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| Challenge: | a new model for compositional questions is needed to answer multi-step reasoning . the model is inspired by formal approaches to compositional semantics . |
| Approach: | They propose an end-to-end differentiable model for interpreting compositional questions . they build a latent tree of interpretable expressions over a sentence . |
| Outcome: | The proposed model outperforms RNN encoders when test questions are longer than training questions. |
Cross-Pair Text Representations for Answer Sentence Selection (D18-1)
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| Challenge: | Existing approaches to textual entailment and question answering focus on intra-pair similarity . a simple lexical matching (marked with italics) is not enough to learn a model based on intrapair Qto-A similarities. |
| Approach: | They propose to compute scalar products representing similarity between members of different pairs instead of using a single vector for each pair. |
| Outcome: | The proposed approach outperforms more complex models based on neural networks. |
QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
| Outcome: | The proposed dataset underperforms humans in a number of reference models . the dataset contains 14K information-seeking dialogs over sections from Wikipedia . |
Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)
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| Challenge: | Existing KBQA methods focus on simpler questions and do not work well on complex questions . a knowledge-based question answering approach is able to answer complex questions using a standard knowledge base . |
| Approach: | They propose to encode query structure into a uniform vector representation of a question and its semantic components into . |
| Outcome: | The proposed approach outperforms existing methods on complex questions while staying competitive on simple questions. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
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| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (D18-1)
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| Challenge: | Existing dependency-based models neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively. |
| Approach: | They propose an extension of graph convolutional networks that is tailored for relation extraction by pruning dependency trees too aggressively. |
| Outcome: | The proposed model outperforms existing sequence and dependency-based models on the large-scale TACRED dataset and has complementary strengths to sequence models. |
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction (D18-1)
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| Challenge: | Existing approaches to label large-scale data are inadequate for distantly supervised relation extraction (DS-RE). |
| Approach: | They propose a multi-level structured (2-D matrix) self-attention mechanism for DS-RE using bidirectional recurrent neural networks. |
| Outcome: | The proposed framework significantly outperforms baselines on two publicly available DS-RE datasets in terms of PR curves, P@N and F1 measures. |
N-ary Relation Extraction using Graph-State LSTM (D18-1)
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| Challenge: | Existing methods for cross-sentence relation extraction split the input graph into two DAGs, but important information can be lost in the splitting procedure. |
| Approach: | They propose a graph-state LSTM model which uses a parallel state to model each word, recurrently enriching state values via message passing. |
| Outcome: | The proposed model keeps the original graph structure, and speeds up computation by allowing more parallelization. |
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (D18-1)
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| Challenge: | Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs . |
| Approach: | They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction . |
| Outcome: | The proposed model outperforms baseline models on a large-scale dataset. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
Large-scale Exploration of Neural Relation Classification Architectures (D18-1)
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| Challenge: | Existing studies on relation classification have been limited to a very narrow range of datasets, making comparisons between approaches difficult. |
| Approach: | They propose a multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and provides direct insights into the challenges faced by language models on relation classification. |
Possessors Change Over Time: A Case Study with Artworks (D18-1)
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| Challenge: | Existing methods to extract possession relations from Wikipedia articles can be used to extract possessors over time. |
| Approach: | They propose to extract possession relations from Wikipedia articles and temporal information indicating when these relations are true. |
| Outcome: | The proposed annotation scheme yields many possessors over time for a given artwork, and an LSTM ensemble can automate the task. |
Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution (D18-1)
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| Challenge: | Existing work on exophoric reference resolution for situated dialogs is limited to a literary model . et al., 2010) showed that it is possible to improve dialogic reference resolving by incrementally adapting word semantic model parameters to idiosyncratic language use by dyad partners. |
| Approach: | They propose to use a logistic regression model to adapt a model to idiosyncratic language . they first train a log regression model and then use it to learn the general referring ability of each word . |
| Outcome: | The proposed methods improve dialogic reference resolution without annotation of referring expressions even with little background data. |
Subgoal Discovery for Hierarchical Dialogue Policy Learning (D18-1)
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| Challenge: | Existing methods to develop dialogue agents for complex tasks require sparse reward signals. |
| Approach: | They propose a divide-and-conquer approach that exploits the hidden structure of a task . they use subgoals to divide a goal-oriented task into simpler subgoal sets . |
| Outcome: | The proposed approach performs competitively against state-of-the-art methods that require human-defined subgoals. |
Supervised Clustering of Questions into Intents for Dialog System Applications (D18-1)
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| Challenge: | Existing methods for detecting intents in text are task-specific and costly . current methods focus on manually analyzing user questions and creating a taxonomy of intents to be attached to the appropriate actions. |
| Approach: | They propose a model for automatically clustering questions into user intents to help design tasks . they use powerful semantic classifiers and supervised clustering methods based on structured output . |
| Outcome: | The proposed model improves on two intent clustering corpora on two languages/domains. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
Decoupling Strategy and Generation in Negotiation Dialogues (D18-1)
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| Challenge: | Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy. |
| Approach: | They propose a modular approach that decouples strategy and generation by coarse dialogue acts . they test their approach on a recently proposed DEALORNODEAL game . |
| Outcome: | The proposed approach can decouple strategy and generation without degeneracy. |
Large-scale Cloze Test Dataset Created by Teachers (D18-1)
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| Challenge: | Existing cloze tests are used to evaluate language proficiency in language exams, but they are not yet available. |
| Approach: | They propose to create a large-scale human-created cloze test dataset CLOTH, which contains questions used in middle-school and high-school language exams. |
| Outcome: | The proposed dataset outperforms existing models and shows that humans outperformed existing models by a significant margin. |
emrQA: A Large Corpus for Question Answering on Electronic Medical Records (D18-1)
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| Challenge: | Existing annotations for other NLP tasks are used to generate domain-specific large-scale question answering (QA) datasets. |
| Approach: | They propose to re-purpose existing annotations for other NLP tasks by generating a large-scale question answering corpus using 1 million questions-logical form and 400,000+ question-answer evidence pairs. |
| Outcome: | The proposed model can be trained to learn domain-specific large-scale question answering (QA) datasets. |
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (D18-1)
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
| Challenge: | Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. |
| Approach: | They propose a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) the questions provide sentence-level supporting facts required for reasoning; and (4) a type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. |
| Outcome: | The proposed dataset has 113k Wikipedia-based question-answer pairs and four key features that make it challenging for the latest QA systems. |
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering (D18-1)
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| Challenge: | Existing QA datasets focus on linguistic understanding, but OpenBookQA probes deeper understanding of topic and language. |
| Approach: | They propose a dataset modeled after open book exams for question answering . the open book is a set of 1326 elementary level science facts . human performance on OpenBookQA is close to 92%, they show . |
| Outcome: | The proposed dataset is modeled after open book exams for question answering . human performance on OpenBookQA is close to 92%, but many state-of-the-art QA methods perform poorly . |
Evaluating Theory of Mind in Question Answering (D18-1)
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| Challenge: | a dataset is proposed for question answering models with respect to their capacity to reason about beliefs. |
| Approach: | They propose a dataset for evaluating question answering models with respect to their capacity to reason about beliefs. |
| Outcome: | The proposed dataset is inspired by theory-of-mind experiments that examine whether children are able to reason about beliefs of others. |
A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)
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| Challenge: | Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks . |
| Approach: | They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks. |
| Outcome: | The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese. |
Semantics as a Foreign Language (D18-1)
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| Challenge: | (2017): Syntactic grammars capture propositions, but graph-based representations aim to capture a wider notion of propositions. |
| Approach: | They propose a neural sequence-to-sequence framework which can recover syntactic linearizations by a sequence-based approach. |
| Outcome: | The proposed framework performs almost on-par with previous state-of-the-art approaches while requiring less parallel training annotations. |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Dependency-based Hybrid Trees for Semantic Parsing (D18-1)
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| Challenge: | Existing models for semantic parsing focus on structure-based models, but none deal with dependency information. |
| Approach: | They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations. |
| Outcome: | The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable. |
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations (D18-1)
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| Challenge: | Existing learning approaches for parsing from denotations (SpFD) do not provide access to correct representations, so there are two steps for every training example. |
| Approach: | They propose a framework for parsing from denotations that generalizes three different learning algorithms. |
| Outcome: | The proposed framework outperforms previous work by 5.0% absolute on exact match accuracy on a question answering dataset. |
Sentence Compression for Arbitrary Languages via Multilingual Pivoting (D18-1)
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| Challenge: | a new study advocates the use of bilingual corpora for sentence compression models . previous work focused on word deletion, while others view sentence compression as a general text rewriting problem. |
| Approach: | They propose to use bilingual corpora for training sentence compression models. |
| Outcome: | The proposed model can be trained for any language as long as a bilingual corpus is available . it performs arbitrary rewrites without access to compression specific data . |
Unsupervised Cross-lingual Transfer of Word Embedding Spaces (D18-1)
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| Challenge: | Existing methods for cross-lingual word mapping require cross-linguistic supervision, but this is not available for many low resource languages. |
| Approach: | They propose an unsupervised method that learns transformation functions over corresponding word embedding spaces using a distributed distributional matching algorithm. |
| Outcome: | The proposed method performs better on bilingual lexicon induction and cross-lingual word similarity prediction datasets than other supervised and unsupervised methods. |
XNLI: Evaluating Cross-lingual Sentence Representations (D18-1)
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Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov
| Challenge: | State-of-the-art natural language processing systems rely on annotated data to learn competent models. |
| Approach: | They extend the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including Swahili and Urdu. |
| Outcome: | The proposed evaluation set extends the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages including low-resource languages such as Swahili and Urdu. |
Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)
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| Challenge: | Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision . |
| Approach: | They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly. |
| Outcome: | The proposed approach significantly improves on the current state-of-the-art in 8 languages. |
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)
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| Challenge: | a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment. |
| Approach: | They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues . |
| Outcome: | The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining. |
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
| Approach: | They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them. |
| Outcome: | The proposed model can distinguish between homographic pun and non-homographic pun texts. |
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check (D18-1)
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| Challenge: | Chinese spelling check (CSC) is a challenging but meaningful task that serves as a preprocessing in many natural language processing(NLP) applications. |
| Approach: | They propose to construct Chinese spelling check corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to OCR- and ASR-based methods. Experimental results demonstrate the effectiveness of the approach. |
| Outcome: | The proposed method is based on visual or phonologically similar spelling errors, and is validated with respect to three standard test sets. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
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| Challenge: | Grammatical error correction systems are expected to correct most learners’ writing errors, but in practice they often produce spurious corrections and fail to correct many errors, thereby misleading learners. |
| Approach: | They propose to use supervised learning to estimate the quality of GEC output sentences to help instructors decide whether to correct the errors or ignore them altogether. |
| Outcome: | The proposed model improves on a feature-based baseline and shows that the state-of-the-art system can be improved when quality scores are used as features for re-ranking the N-best candidates. |
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)
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| Challenge: | Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles. |
| Approach: | They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles. |
| Outcome: | The proposed method achieves better performance than state-of-the-art methods on three different datasets. |
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)
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Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
| Challenge: | a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language. |
| Approach: | They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit. |
| Outcome: | The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. |
A Challenge Set and Methods for Noun-Verb Ambiguity (D18-1)
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| Challenge: | English part-of-speech taggers make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. |
| Approach: | They propose to use a WSJ dataset to identify 30,000 examples of noun-verb ambiguity . they find that english part-of-speech taggers make egregious errors related to nouns and verbs . |
| Outcome: | The proposed model improves on the WSJ Penn Treebank by 14% and 52% relative to the previous model. |
What do character-level models learn about morphology? The case of dependency parsing (D18-1)
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| Challenge: | morphologically rich languages require character-level input models to learn morphology, but some models are poor at disambiguating some words . authors of this study show that character- level models learn a lot from input input . explicit modeling of morphologies is expensive and expensive, authors say . |
| Approach: | They compare character-level models to an oracle with explicit morphological analysis . they show that explicitly modeling morphology improves their best model . |
| Outcome: | The results show that character-level models learn morphology better than word models . the authors compare character-based models to oracles on 12 languages with morphological typologies . |
Learning Better Internal Structure of Words for Sequence Labeling (D18-1)
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| Challenge: | a gap exists between methods for learning representations of sentences and words . authors propose a convolutional neural architecture with no down-sampling for learning words based on character embeddings . |
| Approach: | They propose a funnel-shaped wide convolutional neural architecture with no down-sampling for learning words' internal structure. |
| Outcome: | The proposed model outperforms other character embedding models on six sequence labeling datasets. |
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (D18-1)
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| Challenge: | Existing studies do not explicitly consider inter-personal influences that thrive in the emotional dynamics of dialogues. |
| Approach: | They propose a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. |
| Outcome: | The proposed model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets. |
Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation (D18-1)
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| Challenge: | Existing methods for object detection only handle pre-specified classes, requiring large amounts of visual samples for training. |
| Approach: | They propose a method to retrieve and localize objects specified by a textual query from one million images in 0.5 seconds with high precision. |
| Outcome: | The proposed method can retrieve and localize objects specified by a textual query from one million images in 0.5 seconds with high precision. |
Grounding Semantic Roles in Images (D18-1)
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| Challenge: | Experimental results show that visual semantic role labeling is useful for text understanding . image-based role annotations are prohibitive, but the model induces frame-semantic visual representations . |
| Approach: | They propose to train a visual semantic role labeling model without prohibitive image annotations . they render candidate participants as image regions of objects and train vSRL model which learns to ground roles in the regions which depict the corresponding participant . |
| Outcome: | The proposed model trains without prohibitive image-based role annotations without prohibiting image-related annotations. |
Commonsense Justification for Action Explanation (D18-1)
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| Challenge: | a recent study examines the commonsense reasoning used by humans to justify an AI prediction. |
| Approach: | They propose an approach that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. |
| Outcome: | The proposed model achieves significantly higher performance in both action prediction and justification. |
Learning Personas from Dialogue with Attentive Memory Networks (D18-1)
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| Challenge: | Existing systems that can infer persona from dialogue can be used for computational narrative analysis and personalized dialogue generation. |
| Approach: | They propose neural models to learn persona embeddings in a character trope classification task using IMDB dialogue snippets. |
| Outcome: | The proposed methods could be applied to other domains, including personalized dialogue generation. |
Grounding language acquisition by training semantic parsers using captioned videos (D18-1)
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| Challenge: | a new method for parsing sentences using captioned videos is being developed . we use video clips to ground the semantics of language, but without annotations . |
| Approach: | They develop a semantic parser that is trained in a grounded setting using captioned videos . they use a corpus of sentences paired with videos without other annotations to train it . |
| Outcome: | The proposed parser recovers the meaning of English sentences despite no annotations . learning a grounded semantic parsers can expand the range of data that parseurs can be trained on . |
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation (D18-1)
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Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
| Challenge: | Existing models for translating free-form natural language instructions to a high-level plan for behavioral robot navigation are difficult due to the variability in the way people describe routes. |
| Approach: | They propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for robot navigation. |
| Outcome: | The proposed model significantly outperforms baseline approaches on a new dataset containing 10,050 pairs of navigation instructions. |
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction (D18-1)
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| Challenge: | Existing models that map from inputs to actions are inefficient and require hand-crafted meaning representations. |
| Approach: | They propose to decompose instruction execution to goal prediction and action generation . they introduce two benchmarks for instruction following: LANI and CHAI . |
| Outcome: | The proposed model decomposes instruction execution to goal prediction and action generation. |
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects (D18-1)
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| Challenge: | a confound exists in time series data that violates assumptions of linear models . time series may violate assumptions through temporal diffusion . |
| Approach: | They propose a statistical model that borrows from digital signal processing to fit latent impulse response functions of arbitrary shape. |
| Outcome: | The proposed model recovers true latent IRFs and improves prediction quality . it is based on a new technique that borrows from digital signal processing . |
Is this Sentence Difficult? Do you Agree? (D18-1)
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| Challenge: | a crowdsourcing-based approach to model sentence complexity is proposed . word-level predictors shown to correlate with greater processing difficulties are e.g. word frequency, age of acquisition, root frequency effect, orthographic neighbourhood frequency . |
| Approach: | They propose a crowdsourcing-based approach to model human perception of sentence complexity using a corpus of sentences rated with judgments of complexity for two typologically-different languages. |
| Outcome: | The proposed model predicts agreement among annotators independently from the assigned judgment and the perception of sentence complexity in Italian and English. |
Neural Transition Based Parsing of Web Queries: An Entity Based Approach (D18-1)
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| Challenge: | Existing query parsers that account for the unique grammar of web queries rely on resources not available outside of big web corporations. |
| Approach: | They propose a biLSTM query parser that explicitly accounts for the unique grammar of queries. |
| Outcome: | The proposed query parser outperforms existing state-of-the-art parsers on 2500 annotated queries. |
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing (D18-1)
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| Challenge: | Existing studies have shown that character models are less important in the presence of word embeddings, but combining them quickly leads to diminishing returns. |
| Approach: | They propose to combine pre-trained word embeddings, character models and POS tags to improve parsing quality by categorising words by frequency, POS tag and language. |
| Outcome: | The proposed system improves on initialised word embeddings but combines them quickly leads to diminishing returns. |
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (D18-1)
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| Challenge: | Recent attempts to improve grammar induction systems by bounding complexity of the model have not been compared against unbounded induction models. |
| Approach: | They propose to use a Bayesian induction inducer to limit the search space of the model and then sample trees with or without bounding. |
| Outcome: | The proposed model produces trees more accurately than or competitively with state-of-the-art constituency grammar induction models. |
Incremental Computation of Infix Probabilities for Probabilistic Finite Automata (D18-1)
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| Challenge: | Probabilistic finite automata are used to model distributions in natural language processing . a method that computes infix probabilities incrementally is proposed . |
| Approach: | They propose a method that computes infix probabilities incrementally for probabilistic finite automata . they propose to represent all the probabilities of matching strings as a series of transition matrix calculations . |
| Outcome: | The proposed method is theoretically faster than the previous method and better in practice. |
Syntax Encoding with Application in Authorship Attribution (D18-1)
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| Challenge: | Existing approaches to extract syntactic features from text or sentences are limited by the loss of rich structural information contained in the syntax tree. |
| Approach: | They propose to embed the syntax parse tree of sentence into a learnable distributed representation . they show that the approach improves upon the prior art and achieves new performance records . |
| Outcome: | The proposed approach improves upon the prior art and achieves new performance records on five benchmarking data sets. |
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks (D18-1)
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| Challenge: | Using end-to-end neural network models, Sanskrit is tokenized by splitting compounds and resolving phonetic merges. |
| Approach: | They propose end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges. |
| Outcome: | The proposed models outperform the state-of-the-art for the task of splitting compounds and resolving phonetic merges. |
Session-level Language Modeling for Conversational Speech (D18-1)
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| Challenge: | Xiong et al., 2017) generalizes language models for conversational speech recognition . recurrent neural networks (RNNs) read a list of words sequentially and predict the next word at each position. |
| Approach: | They propose to generalize language models for conversational speech recognition to capture conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. |
| Outcome: | The proposed model reduces perplexity and improves word error rate over standard models in the conversational telephone speech domain. |
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)
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| Challenge: | Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses . |
| Approach: | They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query. |
| Outcome: | The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses. |
Training Millions of Personalized Dialogue Agents (D18-1)
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| Challenge: | Current dialogue systems fail at being engaging for users when trained end-to-end without relying on proactive reengaging scripted strategies. |
| Approach: | They propose a dataset that provides 5 million personas and 700 million person-based dialogues. |
| Outcome: | The proposed dataset provides 5 million personas and 700 million person-based dialogues. |
Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (D18-1)
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| Challenge: | Existing work on task oriented dialog systems has limited expressive power to one intent per query and one slot label per token. |
| Approach: | They propose a hierarchical annotation scheme for semantic parsing that allows representation of compositional queries. |
| Outcome: | The proposed representation outperforms sequence-to-sequence approaches on a 44k annotated query dataset. |
The glass ceiling in NLP (D18-1)
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| Challenge: | a glass ceiling exists within the field of NLP, but no study has examined this issue . female representation in Computer Science is lower than the average STEM field . |
| Approach: | They propose to use a mathematical model to show that a glass ceiling exists in NLP . they find that there is a growing mentor gender gap and a disparity between mentors . |
| Outcome: | The proposed model shows that a glass ceiling exists within the field of NLP since the mid 2000s. |
Reducing Gender Bias in Abusive Language Detection (D18-1)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
| Approach: | They propose to use debiased word embeddings, gender swap data augmentation to reduce model bias . they also propose to fine-tune models with a larger corpus to correct such bias if needed . |
| Outcome: | The proposed methods reduce model bias by 90-98% and can be extended to correct model bias in other scenarios. |
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories (D18-1)
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| Challenge: | With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. |
| Approach: | They propose to use CNN-RNN model to automatically categorize and analyze sexual harassment data from SafeCity forums. |
| Outcome: | The proposed model achieves an accuracy of 86.5% for groping, ogling, and commenting, and 82.5% in multi-label models. |
Learning multiview embeddings for assessing dementia (D18-1)
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| Challenge: | In 2017, 5.7 million Americans were living with Alzheimer's disease (AD), and the disease accounted for $11.4 billion in healthcare costs in the United States. |
| Approach: | They leverage the multiview nature of a small AD dataset to learn an embedding that captures different modes of cognitive impairment. |
| Outcome: | The proposed embeddings achieve an F1 score of 0.82 and a mean absolute error of 3.42 in the classification task and predicting clinical scores. |
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community (D18-1)
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Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon
| Challenge: | Compared to large-scale collections of conversations from social media, Wikipedia talk pages only capture a subset of all discussions and only accounts for the final form of each conversation. |
| Approach: | They propose to reconstruct a corpus that encompasses the complete history of conversations between Wikipedia contributors. |
| Outcome: | The proposed corpus extracts high quality data in both Chinese and English. |
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (D18-1)
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| Challenge: | Existing large labeled text datasets contain labels for multiple subsets of biomedical entity types, but it is rare to find large labeling datasets containing all desired entity types together. |
| Approach: | They propose a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. |
| Outcome: | The proposed method improves NER F1 over training in isolation on biocreative V CDR, biocreativ VI ChemProt and MedMentions datasets. |
Adversarial training for multi-context joint entity and relation extraction (D18-1)
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| Challenge: | Existing models that use adversarial training (AT) have been used in various tasks such as parsing, POS tagging, relation extraction and translation. |
| Approach: | They propose to use adversarial training (AT) to regularize neural network methods by adding small perturbations to the input data. |
| Outcome: | The proposed model improves state-of-the-art on news, biomedical, and real estate datasets and for different languages. |
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding (D18-1)
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| Challenge: | Existing methods for tagging unstructured texts with arbitrary number of terms drawn from an ontology are lacking. |
| Approach: | They propose a model for tagging unstructured texts with an arbitrary number of terms drawn from an ontology. |
| Outcome: | The proposed model yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
Evaluating the Utility of Hand-crafted Features in Sequence Labelling (D18-1)
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| Challenge: | Conventional wisdom is that hand-crafted features are redundant for deep learning models . authors propose a method for using handcrafted features in a hybrid learning approach . |
| Approach: | They propose a method for exploiting handcrafted features as part of a hybrid learning approach. |
| Outcome: | The proposed method outperforms baseline models on a named entity recognition task and reduces training requirements to 60% while maintaining the same predictive accuracy. |
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data (D18-1)
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| Challenge: | Pre-trained word embeddings and language models cannot capture word connections in a sentence. |
| Approach: | They propose to implicitly capture word connections from unlabeled data by word ordering model with self-attention mechanism. |
| Outcome: | The proposed model achieves 96.35% UAS and 95.25% LAS on the English PTB dataset. |
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)
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| Challenge: | a measure of morphological complexity is used to characterize syntactic information in word embeddings. |
| Approach: | They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance. |
| Outcome: | The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker. |
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation (D18-1)
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| Challenge: | Neural sequence-to-sequence models have proven effective for machine translation, but at the expense of interpretability. |
| Approach: | They analyze how morphological features are captured at different levels of the NMT encoder while varying the target language. |
| Outcome: | The proposed model is not interpretable, but only captures morphological features in context and only to the extent they are directly transferable to the target words. |
Imitation Learning for Neural Morphological String Transduction (D18-1)
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| Challenge: | Recent studies have shown that neural transition-based models can be used for morphological tasks such as inflection generation and lemmatization without a character aligner or warm start. |
| Approach: | They propose to use imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. |
| Outcome: | The proposed model eliminates the need for a character aligner or warm start and achieves state-of-the-art performance on several datasets. |
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem (D18-1)
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| Challenge: | a Paradigm cell filling problem is a problem that asks how speakers of a language can reliably produce inflectional forms without ever witnessing them before. |
| Approach: | They implement novel neural models for the Paradigm Cell Filling Problem in morphology . they evaluate models on 18 data sets in 8 languages and implement them in a new dataset . |
| Outcome: | The proposed model performs comparable to previous work with less training data. |
Generating Natural Language Adversarial Examples (D18-1)
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| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |
Multi-Head Attention with Disagreement Regularization (D18-1)
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| Challenge: | Existing methods to encourage diversity among multi-head attention are limited. |
| Approach: | They propose a disagreement regularization term to encourage diversity among attention heads . they validated their approach on EnglishGerman and ChineseEnglish translation tasks . |
| Outcome: | The proposed approach improves translation performance across language pairs on English-German and Chinese-English translation tasks. |
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (D18-1)
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| Challenge: | Existing studies on Active Learning (AL) for natural language processing have limited data requirements. |
| Approach: | They propose a Bayesian active learning approach that reduces deep learning's data dependence by comparing models and acquisition functions. |
| Outcome: | The proposed approach outperforms i.i.d. baselines and is more efficient than other approaches. |
Bayesian Compression for Natural Language Processing (D18-1)
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| Challenge: | In natural language processing, recurrent neural networks have a huge number of parameters. |
| Approach: | They propose a Bayesian sparsification technique which allows compressing RNNs dozens or hundreds of times without time-consuming hyperparameters tuning. |
| Outcome: | The proposed technique compresses the RNN dozens or hundreds of times without time-consuming hyperparameters tuning. |
Multimodal neural pronunciation modeling for spoken languages with logographic origin (D18-1)
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| Challenge: | Graphemes of most languages encode pronunciation, though some are more explicit than others . pronunciation modeling in logographic languages requires decomposing logographs into subunits . |
| Approach: | They propose a multimodal approach to predict pronunciation of Cantonese logographic characters using neural networks. |
| Outcome: | The proposed framework improves performance by 18.1% and 25.0% respectively to unimodal and multimodal baselines. |
Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet (D18-1)
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| Challenge: | Chinese pinyin input method engine (IME) converts pinyine into character based on its core component, pinyan-to-character conversion (P2C). |
| Approach: | They propose a sequence-to-sequence model with gated-attention mechanism for Chinese IMEs. |
| Outcome: | The proposed model improves on existing models in benchmark datasets showing great user experience improvement compared to traditional models. |
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models (D18-1)
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| Challenge: | Recurrent neural network language models (RNNs) only estimate probabilities for complete sequences of text, whereas some applications require context-independent phrase probabilities instead. |
| Approach: | They propose a method to alter the RNNLM training to make it more accurate at marginal estimation. |
| Outcome: | The proposed method is effective compared to baselines including the traditional RNNLM probability and importance sampling approach. |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
The Importance of Generation Order in Language Modeling (D18-1)
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| Challenge: | Neural language models are universally autoregressive, generating sentences one token at a time from left to right. |
| Approach: | They propose a two-pass language model that generates partially-filled sentences and fills in missing tokens. |
| Outcome: | The proposed model produces partially-filled sentences and fills in missing tokens. |
Document-Level Neural Machine Translation with Hierarchical Attention Networks (D18-1)
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| Challenge: | Neural machine translation (NMT) can be improved by including document-level contextual information. |
| Approach: | They propose a hierarchical attention model that captures document-level contextual information and conditioning on the NMT model’s own hidden states. |
| Outcome: | The proposed model improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods and that both the encoder and decoder benefit from context in complementary ways. |
Three Strategies to Improve One-to-Many Multilingual Translation (D18-1)
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| Challenge: | Existing studies show that one-to-many multilingual translation cannot perform on par with the individually trained models. |
| Approach: | They propose to exploit unique initial states for target languages and language-dependent positional embeddings to create hidden cells of the encoder to achieve comparable or even better performance than individually trained models. |
| Outcome: | The proposed methods achieve comparable or even better performance than the individually trained models. |
Multi-Source Syntactic Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to integrate source syntax into neural machine translations use linearized parses. |
| Approach: | They propose a linearized parsed neural machine translation technique that integrates source syntax into neural machine learning. |
| Outcome: | The proposed model improves over seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. |
Fixing Translation Divergences in Parallel Corpora for Neural MT (D18-1)
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| Challenge: | Existing methods to detect translation divergences from parallel corpora are noisy and limited in size. |
| Approach: | They propose an unsupervised method for detecting translation divergences in parallel sentences . they use a neural network that computes cross-lingual sentence similarity scores . |
| Outcome: | The proposed method improves translation performance for English-French and English-German translation tasks. |
Adversarial Evaluation of Multimodal Machine Translation (D18-1)
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| Challenge: | Existing evidence that visual context helps multimodal machine translation systems is unconvincing due to inconsistencies between text-similarity metrics and human judgements. |
| Approach: | They propose an adversarial evaluation method to examine the utility of image data in multimodal machine translation. |
| Outcome: | The proposed method shows that only one out of three publicly available systems is sensitive to this perturbation of the data. |
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion (D18-1)
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| Challenge: | Existing approaches to learn orthogonal matrix aligning bilingual lexicons are suboptimal . resulting models suffer from "hubness problem" because word vectors tend to be nearest neighbors of abnormally high number of other words. |
| Approach: | They propose a unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. |
| Outcome: | The proposed approach outperforms the state-of-the-art on word translation on standard benchmarks. |
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy . |
| Approach: | They propose a model with a mechanism to control the softness of attention by means of an attention temperature. |
| Outcome: | The proposed model outperforms baseline models on Chinese-English and English-Vietnamese translations. |
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation (D18-1)
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| Challenge: | In order to achieve faster training we increase the mini-batch size and scale the learning rate accordingly. |
| Approach: | They propose a technique that delays gradient updates by increasing the mini-batch size to improve the model's convergence. |
| Outcome: | The proposed technique can train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU. |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)
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| Challenge: | Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics . |
| Approach: | They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach. |
| Outcome: | The proposed approach improves translation performance and accuracy of DP predictions. |
Getting Gender Right in Neural Machine Translation (D18-1)
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| Challenge: | linguistics studies show that the language used by males and females differs in terms of style and syntax. |
| Approach: | They integrate gender information into NMT systems to improve translation quality for multiple language pairs by incorporating gender information to a large dataset. |
| Outcome: | The proposed system significantly improves translation quality for some language pairs. |
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation (D18-1)
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| Challenge: | Existing models treat source and target sentences as one-dimensional sequences over time, while a 2D mapping is achieved using an MDLSTM layer. |
| Approach: | They propose a multi-dimensional long short-term memory architecture for translation modelling that uses an MDLSTM layer to define the correspondence between source and target words. |
| Outcome: | The proposed model improves on two WMT 2017 tasks, showing that the source and target sentences are aligned with each other in a 2D grid. |
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (D18-1)
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| Challenge: | Autoregressive decoding is the only part of sequence-to-sequence models that prevents massive parallelization at inference time. |
| Approach: | They propose a non-autoregressive architecture based on connectionist temporal classification . they conduct experiments on the WMT English-Romanian and English-German datasets . |
| Outcome: | The proposed model achieves a significant speedup over autoregressive models . the model can be trained end-to-end and maintains translation quality comparable to other models compared to autoregression models based on connectionist temporal classification . |
Prediction Improves Simultaneous Neural Machine Translation (D18-1)
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| Challenge: | Current systems for simultaneous machine translation use an AGENT to control an incremental encoder-decoder model. |
| Approach: | They propose a general-purpose prediction action which predicts future words in the input stream. |
| Outcome: | The proposed agent with prediction has better translation quality and less delay compared to an agent-based system without prediction. |
Training Deeper Neural Machine Translation Models with Transparent Attention (D18-1)
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| Challenge: | Existing NMT models are shallow in comparison to convolutional models used for both text and vision tasks. |
| Approach: | They propose to modify the attention mechanism to ease the optimization of deeper models by a simple modification to the seq2seq with attention paradigm. |
| Outcome: | The proposed model achieves consistent gains of 0.7-1.1 BLEU on the benchmark WMT’14 English-German and WMT'15 Czech-English tasks. |
Context and Copying in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation systems with subword vocabularies can translate or copy unknown words . we examine the influence of context and subword features on copying behavior . |
| Approach: | They show that neural machine translation systems with subword vocabularies can translate unknown words . they also learn to copy words based on context and features of the words themselves . |
| Outcome: | The proposed model outperforms phrase-based statistical machine translation systems on translation of unknown words. |
Encoding Gated Translation Memory into Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (MT) technology has made significant progress in the past few years. |
| Approach: | They propose a method to combine the strengths of TM and neural machine translation (NMT) they use a gating mechanism to balance the impact of the TM match on the NMT decoder . |
| Outcome: | The proposed method improves translation quality by over 10 BLEU points when fuzzy matches are higher than 50% on the UN corpus. |
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (D18-1)
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| Challenge: | Existing approaches to inducing APE have suffered from over-correction, where the APE system tends to keep the machine translated text without any modification. |
| Approach: | They propose a neural programmer-interpreter approach to automated post-editing (APE) that mimics human perform post- editing using discrete edit operations . their model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
| Outcome: | The proposed model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing (D18-1)
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| Challenge: | Accurate and complete knowledge bases (KBs) are paramount in NLP. |
| Approach: | They employ multiview learning for increasing the accuracy and coverage of entity type information in KBs by taking high- and low-resource languages from Wikipedia. |
| Outcome: | The proposed learning improves the accuracy and coverage of knowledge bases (KBs) by combining language and representation. |
Word Embeddings for Code-Mixed Language Processing (D18-1)
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| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging tasks. |
On the Strength of Character Language Models for Multilingual Named Entity Recognition (D18-1)
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| Challenge: | Character-level patterns have been widely used in English Named Entity Recognition systems. |
| Approach: | They propose to use corpus-agnostic character-level language models to capture name tokens . they demonstrate they can capture name and non-name tokens in a diverse set of languages . |
| Outcome: | The proposed model improves the performance of an off-the-shelf NER system for multiple languages. |
Code-switched Language Models Using Dual RNNs and Same-Source Pretraining (D18-1)
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| Challenge: | Using recurrent neural networks to build language models for code-switched text is an important problem with implications to downstream applications such as speech recognition and machine translation. |
| Approach: | They propose a novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately and a generative model estimated using the training data. |
| Outcome: | The proposed techniques yield significant reductions in perplexity on Mandarin-English task and improve on baseline models. |
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification (D18-1)
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| Challenge: | Code-switching is a challenge for NLP due to the lack of representative data for training models. |
| Approach: | They propose a model that is trained exclusively on monolingual resources but can be applied to unseen code-switched text at inference time. |
| Outcome: | The proposed model outperforms standard models on Hindi-English part-of-speech tagging and on unannotated code-switched text with alternate scripts. |
Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)
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| Challenge: | Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved. |
| Approach: | They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available. |
| Outcome: | The proposed model can discriminate emerging intents when no labeled utterances are available in training data. |
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (D18-1)
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| Challenge: | Existing sentences classification models often classify sentences in isolation without considering the context in which sentences appear. |
| Approach: | They propose a hierarchical sequential labeling network to make use of contextual information within surrounding sentences to help classify the current sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Topic Memory Networks for Short Text Classification (D18-1)
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| Challenge: | Existing classification models for short texts are weak due to data sparsity . |
| Approach: | They propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. |
| Outcome: | The proposed model outperforms state-of-the-art models on short text classification, while generating coherent topics. |
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
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| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |
Variational Autoregressive Decoder for Neural Response Generation (D18-1)
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| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
| Approach: | They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence. |
| Outcome: | Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets. |
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)
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| Challenge: | Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. |
| Approach: | They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification. |
| Outcome: | The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show . |
Learning Neural Templates for Text Generation (D18-1)
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| Challenge: | Encoder-decoder models are uninterpretable and difficult to control in terms of content. |
| Approach: | They propose a neural generation system using a hidden semi-markov model which learns latent templates jointly with learning to generate. |
| Outcome: | The proposed model learns useful templates and achieves strong performance nearing that of encoder-decoder models. |
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)
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| Challenge: | Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available. |
| Approach: | They propose an algorithm to generate exponentially many pseudo-references by compressing existing references into lattices and traversing them to generate new pseudo-References. |
| Outcome: | The proposed model significantly improves on baselines in machine translation and image captioning, and is comparable to existing models. |
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |
Embedding Multimodal Relational Data for Knowledge Base Completion (D18-1)
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| Challenge: | Existing approaches focus on a finite set of entities, ignoring the variety of data types used in knowledge bases. |
| Approach: | They propose multimodal knowledge base embeddings that use different neural encoders for observed data and different neural decoders to learn embedded entities and multimodal data. |
| Outcome: | The proposed models outperform existing methods with 5-7% accuracy over existing methods. |
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (D18-1)
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| Challenge: | Existing relation extraction systems are designed for within-sentence relations, but extracting information from scientific articles requires relations across sentences. |
| Approach: | They propose a multi-task setup for identifying entities, relations, and coreference clusters in scientific articles . they develop a unified framework called SciIE with shared span representations to solve this problem . |
| Outcome: | The proposed model outperforms existing models without domain-specific features in scientific information extraction. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
Multi-Hop Knowledge Graph Reasoning with Reward Shaping (D18-1)
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| Challenge: | Multi-hop reasoning is an effective approach for query answering over incomplete knowledge graphs (KGs). |
| Approach: | They propose to adopt a pretrained one-hop embedding model to estimate reward of unobserved facts and to force agents to explore diverse set of paths using randomly generated edge masks. |
| Outcome: | The proposed model reduces false negative supervision and counters spurious search trajectories by forcing the agent to explore a diverse set of paths using randomly generated edge masks. |
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting (D18-1)
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| Challenge: | Existing lexicons have limited coverage for learning morphological inflection patterns from labeled data. |
| Approach: | They propose two new methods to solve paradigm completion, the morphological task of generating missing forms, given a partial paradigm. |
| Outcome: | The proposed methods outperform the previous state-of-the-art by 9.71% absolute accuracy on a 52-language benchmark dataset. |
Implicational Universals in Stochastic Constraint-Based Phonology (D18-1)
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| Challenge: | Optimality Theory and Harmonic Grammar are constraint-based implementations of phonological theory that do not tamper with typological structure induced by categorical frameworks. |
| Approach: | They propose to model the implicational universals of phonological theory, called T-orders, and to use stochastic constraint-based frameworks to model them. |
| Outcome: | The proposed frameworks do not tamper with typological structure induced by categorical frameworks. |
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? (D18-1)
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| Challenge: | Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn. |
| Approach: | They extend contextual decomposition technique to convolutional neural networks and bidirectional long-term memory networks to evaluate and compare these models for morphological tagging on three morphology-dependent languages. |
| Outcome: | The proposed models implicitly discover understandable linguistic rules for morphological tagging on three morphology-dependent languages. |
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations (D18-1)
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| Challenge: | Existing approaches to generalization to resource-rich languages are difficult . a recent study shows that word representations can be useful in low resource languages . |
| Approach: | They propose two approaches for improving generalization to low-resource languages by adapting continuous word representations using linguistically motivated subword units. |
| Outcome: | The proposed method improves generalization to low resource languages . it requires neither parallel corpora nor bilingual dictionaries and requires no parallel training . |
A Computational Exploration of Exaggeration (D18-1)
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| Challenge: | a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements . |
| Approach: | They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO . |
| Outcome: | The proposed approach can detect exaggerated sentences using a crowdsourced dataset. |
Building Context-aware Clause Representations for Situation Entity Type Classification (D18-1)
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| Challenge: | Existing models for categorizing clauses based on situation entity types do not provide accurate results. |
| Approach: | They propose to build context-aware clause representations for predicting situation entity types of clauses by modeling context influences and inter-dependencies of clause. |
| Outcome: | The proposed model achieves state-of-the-art performance on genre-rich dataset MASC+Wiki . |
Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain (D18-1)
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| Challenge: | Existing methods for reconstructing narratives and thread structures of news articles and discussions are lacking . temporal characteristics, triggering event relations, and meta information are used to solve the problem . |
| Approach: | They propose a Hierarchical Dirichlet Gaussian Marked Hawkes process for reconstructing narratives and thread structures of news articles and discussion posts. |
| Outcome: | The proposed model outperforms baseline models on real-world datasets and Wikipedia conversations. |
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models (D18-1)
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| Challenge: | Scientific publications are argumentative and often adhere to well-trodden rhetorical patterns and argumentation schemes. |
| Approach: | They investigate the link between scientific publications and rhetorical aspects such as discourse categories or citation contexts by coupling rhetorical classifiers with extraction of argumentative components. |
| Outcome: | The proposed models show significant performance gains for different rhetorical analysis tasks. |
Neural Ranking Models for Temporal Dependency Structure Parsing (D18-1)
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| Challenge: | a new neural temporal dependency parser is being developed for news reports and narrative stories . a similar system is used for other NLP applications such as timeline construction . |
| Approach: | They build a neural temporal dependency parser that parses time expressions and events in a text . their results shed light on the nature of temporal relation structures in different domains . |
| Outcome: | The proposed model beats baselines on news reports and narrative stories on two data domains. |
Causal Explanation Analysis on Social Media (D18-1)
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| Challenge: | Understanding causal explanations is an important psychological factor linked to physical and mental health. |
| Approach: | They propose to automate causal explanation analysis by building on discourse parsing and using a hierarchy of Bidirectional LSTMs to identify the specific phrase that is the explanation. |
| Outcome: | The proposed subtasks achieve strong accuracies but differ in their approaches . the proposed sub task is compared with the previous task and is able to identify the specific phrase that is the explanation. |
LRMM: Learning to Recommend with Missing Modalities (D18-1)
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| Challenge: | Existing methods for content-based recommendation with missing or corrupted modalities are lacking in learning multimodal models. |
| Approach: | They propose a multimodal multimodal autoencoder that learns multimodal representations for complementing and imputing missing modalities. |
| Outcome: | The proposed framework achieves state-of-the-art performance on rating prediction tasks and is more robust to previous methods in alleviating data-sparsity and the cold-start problem. |
Content Explorer: Recommending Novel Entities for a Document Writer (D18-1)
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| Challenge: | Existing tools for exploratory search can be useful for document writers but they are not always effective for identifying topics for further research. |
| Approach: | They propose a supervised learning problem for recommending topics to a writer . they propose entropy loss function modification to improve the results . |
| Outcome: | The proposed model improves on a large dataset and can be used to identify topics for further research. |
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
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| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
Thread Popularity Prediction and Tracking with a Permutation-invariant Model (D18-1)
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| Challenge: | a task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. |
| Approach: | They propose a deep neural network architecture to model the expected cumulative reward of a recommendation (action) they employ a greedy procedure to approximate the action that maximizes the predicted Q-value . |
| Outcome: | The proposed approach outperforms the state-of-the-art on five real-world datasets. |
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations (D18-1)
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| Challenge: | Existing models for finding aspects and sentiments in opinionated texts ignore sentiments and are not supervised. |
| Approach: | They propose a probabilistic model that finds aspects and sentiments in opinionated texts . they use authors, discourse relations, and word embeddings to capture regularities . |
| Outcome: | The proposed model outperforms state-of-the-art models in topic cohesion and sentiment classification. |
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking (D18-1)
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| Challenge: | Experimental results show that the extracted emotion-associated topic words represent emotion-evoking events. |
| Approach: | They propose an interpretable neural network approach for relevant emotion ranking . they initialize the hidden layer to approximate the behavior of topic models . |
| Outcome: | The proposed approach performs better than state-of-the-art methods on real-world corpora. |
Multi-grained Attention Network for Aspect-Level Sentiment Classification (D18-1)
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| Challenge: | Existing approaches to aspect sentiment classification use coarse-grained attention mechanisms . a novel approach captures word-level interaction between aspect and context . |
| Approach: | They propose a novel multi-grained attention network model for aspect level sentiment classification . they use a fine-grounded attention mechanism to capture word-level interaction between aspect and context . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on three datasets . it shows that aspect-level interactions can bring extra useful information and improve performance . |
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)
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| Challenge: | Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches. |
| Approach: | They propose a lexicon-driven contextual attention mechanism and a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. |
| Outcome: | The proposed model outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets. |
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)
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Deepanway Ghosal, Md Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs. |
| Approach: | They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction. |
| Outcome: | The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets. |
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
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| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
| Approach: | They propose a semi-supervised learning approach that minimizes the distance between source and target instances in embedded feature space. |
| Outcome: | The proposed approach can improve on baseline methods in various settings. |
ExtRA: Extracting Prominent Review Aspects from Customer Feedback (D18-1)
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| Challenge: | Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects. |
| Approach: | They propose a framework for extracting the most prominent aspects of a given product type from textual reviews. |
| Outcome: | The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision. |
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content (D18-1)
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| Challenge: | rumors with multimedia content are becoming more and more common on social networks . a new feature set is proposed to verify rumors pivoting on multimedia content . |
| Approach: | They propose to use multimedia content to find external information on social media platforms . they propose to leverage semantic similarity between rumors and external information . |
| Outcome: | The proposed approach achieves state-of-the-art results on social networks . it leverages semantic similarity between rumors and external information . |
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts (D18-1)
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| Challenge: | Existing work on explaining classifier decisions has not addressed local feature redundancy . a common way to explain why a model classified an example is to extract a sparse subset of features that were responsible for the decision . |
| Approach: | They propose an adversarial method for producing high-recall explanations of text classifier decisions . they use a method which scans the residual of attention for remaining predictive signal . |
| Outcome: | The proposed method produces high-recall explanations of text classifier decisions . it uses a set of human-annotated personal attacks to evaluate the impact . |
Automatic Detection of Vague Words and Sentences in Privacy Policies (D18-1)
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| Challenge: | a recent study has raised concerns over privacy policies' opaqueness . lack of clarity in privacy policies can lead to undesired ads and privacy breaches . |
| Approach: | They propose to analyze the semantics of vague words and sentences and use them to identify vague content in privacy policies. |
| Outcome: | The proposed methods are effective and provide suggestions for improving privacy policies. |
Multi-view Models for Political Ideology Detection of News Articles (D18-1)
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| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |
Predicting Factuality of Reporting and Bias of News Media Sources (D18-1)
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| Challenge: | a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online . |
| Approach: | They propose to characterize entire news media to predict factuality and bias . they experiment with news websites and a set of features derived from their content . |
| Outcome: | The proposed model shows that the features of news websites perform better than baseline . the results show that the feature types are important for fact-checking systems . |
Legal Judgment Prediction via Topological Learning (D18-1)
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| Challenge: | Existing studies focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks. |
| Approach: | They propose a topological multi-task learning framework that incorporates multiple subtasks and DAG dependencies into judgment prediction. |
| Outcome: | The proposed model improves on baselines on all judgment prediction tasks. |
Hierarchical CVAE for Fine-Grained Hate Speech Classification (D18-1)
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| Challenge: | Existing work on automated hate speech detection focuses on binary classification or on differentiating among a small set of categories. |
| Approach: | They propose a method to discriminate among 40 hate groups of 13 different hate group categories. |
| Outcome: | The proposed method outperforms discriminative models on a fine-grained hate speech classification task. |
Residualized Factor Adaptation for Community Social Media Prediction Tasks (D18-1)
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| Challenge: | Existing approaches to social media language capture only socio-demographic contexts, such as age, education rates, race, and gender. |
| Approach: | They propose a method which integrates community attributes and adapts linguistic features to community attributes. |
| Outcome: | The proposed model integrates community attributes and adapts linguistic features to community attributes. |
Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies (D18-1)
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| Challenge: | Amidst growing concern over media manipulation, NLP studies focus on overt strategies like censorship and “fake news”. |
| Approach: | They propose to use two concepts from political science literature to identify subtler media manipulation strategies . they propose to apply embedding-based methods to cross-lingually project English frames to Russian . |
| Outcome: | The proposed techniques can be applied to 13 years of the Russian newspaper Izvestia and show that they highlight U.S. moral failings and threats to the U.s. |
Identifying the sentiment styles of YouTube’s vloggers (D18-1)
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| Challenge: | Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog’s narrative time. |
| Approach: | They propose to automatically analyze the vlogs' linguistic styles using a dynamic intra-textual approach to sentiment analysis to shed light on the different temporal trajectories used by vloggers. |
| Outcome: | The proposed method predicts that vlogs with positive endings are the most prevalent in the sample. |
Native Language Identification with User Generated Content (D18-1)
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| Challenge: | Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. |
| Approach: | They propose to use linguistically-motivated features and social media characteristics to obtain high accuracy on this task. |
| Outcome: | The proposed method is highly accurate on a social media content where authors are highly-fluent nonnative speakers. |
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)
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| Challenge: | Neural machine translation suffers from exposure bias and error propagation problem. |
| Approach: | They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part . |
| Outcome: | The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models. |
A Study of Reinforcement Learning for Neural Machine Translation (D18-1)
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| Challenge: | Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems. |
| Approach: | They propose to leverage reinforcement learning to boost the performance of NMT systems trained with monolingual data. |
| Outcome: | The proposed method achieves competitive results on translation tasks in English-German, Chinese-English and English-English systems. |
Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
| Approach: | They propose to extend the recently introduced meta-learning algorithm for low-resource neural machine translation (NMT) they frame low-Resource translation as a meta- learning problem where we learn to adapt to low-REsource languages based on multilingual high-resourced language tasks. |
| Outcome: | The proposed meta-learning algorithm outperforms the multilingual, transfer learning based approach and can train a competitive NMT system with only a fraction of training examples. |
Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |
A Visual Attention Grounding Neural Model for Multimodal Machine Translation (D18-1)
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| Challenge: | Existing approaches to multimodal machine translation do not integrate visual information into the translation process. |
| Approach: | They propose a multimodal machine translation model that utilizes parallel visual and textual information. |
| Outcome: | The proposed model outperforms existing methods on the Multi30K and Ambiguous COCO datasets. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
Cross-topic Argument Mining from Heterogeneous Sources (D18-1)
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| Challenge: | Argument mining is a core technology for automating argument search in document collections. |
| Approach: | They propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. |
| Outcome: | The proposed scheme outperforms vanilla BiLSTMs in two- and three-label cross-topic settings and can be further improved by leveraging additional data for topic relevance using multi-task learning. |
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
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| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
| Approach: | They propose a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision. |
| Outcome: | The proposed framework improves over baselines and shows that opinion summaries are preferred by human judges according to multiple criteria. |
CARER: Contextualized Affect Representations for Emotion Recognition (D18-1)
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| Challenge: | Existing methods to model emotion-relevant content are based on rule-based and statistics-based approaches. |
| Approach: | They propose a semi-supervised graph-based algorithm to produce rich structural descriptors . they use word embeddings to evaluate the algorithm on emotion recognition tasks . |
| Outcome: | The proposed method outperforms state-of-the-art methods on emotion recognition tasks. |
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency (D18-1)
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| Challenge: | Conditional models are frequently encountered in practice, but there has not been a rigorous theoretical analysis of NCE in this setting. |
| Approach: | They propose to use a ranking-based and ranking-only method for conditional models to estimate parameter estimates. |
| Outcome: | The proposed method avoids calculation of partition function or derivatives at each training step . it is closely related to negative sampling methods, now widely used in NLP . |
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization (D18-1)
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| Challenge: | Maximum-likelihood estimation (MLE) is widely used for text-generation based natural language processing applications. |
| Approach: | They propose a method to train models with maximum-likelihood estimation using a differentiable surrogate of longest common subsequence measure that captures sequence-level structure similarity. |
| Outcome: | Experimental results show that the proposed approach improves on the current MLE approach for downstream tasks like text summarization and machine translation. |
Pathologies of Neural Models Make Interpretations Difficult (D18-1)
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| Challenge: | Existing methods for NLP use input reduction to determine a word's importance . human accuracy degrades when shown the reduced examples instead of the original . |
| Approach: | They propose a process that iteratively removes the least important word from an input . they show human models make the same predictions with high confidence . |
| Outcome: | The proposed methods expose pathological behaviors of neural models . human experiments show that reduced examples lack information to support the prediction of any label . |
Phrase-level Self-Attention Networks for Universal Sentence Encoding (D18-1)
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| Challenge: | Phrase-level self-attention networks (PSAN) can capture context dependencies at the phrase level instead of the sentence level. |
| Approach: | They propose to perform self-attention across words inside a phrase to capture context dependencies at the phrase level and use the gated memory updating mechanism to refine each word’s representation hierarchically with longer-term context dependency captured in a larger phrase. |
| Outcome: | The proposed model can achieve state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity. |
BanditSum: Extractive Summarization as a Contextual Bandit (D18-1)
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| Challenge: | Existing methods for extractive summarization are heuristically generated and require a set of binary labels to be selected. |
| Approach: | They propose a method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. |
| Outcome: | The proposed method achieves better ROUGE scores than the state-of-the-art methods and significantly fewer update steps than competing approaches. |
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification (D18-1)
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| Challenge: | Current lexical simplification approaches rely on heuristics and corpus level features that do not align with human judgment. |
| Approach: | They propose a human-rated word-complexity lexicon and a neural readability ranking model that uses human ratings to measure the complexity of any given word or phrase. |
| Outcome: | The proposed model performs better than state-of-the-art models for lexical simplification tasks and evaluation datasets. |
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data (D18-1)
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| Challenge: | Existing work on grounded language learning does not capture the semantics of correspondences between structured world state representations and texts. |
| Approach: | They propose to learn explicit latent semantic annotations from paired structured tables and texts . they use an adapted semi-hidden Markov model to impose a soft constraint to further improve performance . |
| Outcome: | The proposed framework improves on a semi-hidden Markov model and extracts templates for language generation. |
Syntactic Scaffolds for Semantic Structures (D18-1)
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| Challenge: | Syntactic scaffolds avoid expensive syntactical processing at runtime . many systems have used syntastic dependency or phrase-based parsers as preprocessing for semantic analysis. |
| Approach: | They propose a multitask learning approach that uses a syntactic treebank to integrate syntaktic information into semantic tasks. |
| Outcome: | The proposed method improves on PropBank semantics, frame semantics and coreference resolution tasks. |
Hierarchical Quantized Representations for Script Generation (D18-1)
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| Challenge: | Scripts define knowledge about how everyday scenarios are expected to unfold . language models tend towards local coherency, which is a major issue . |
| Approach: | They propose an autoencoder model with a latent space defined by a hierarchy of categorical variables . they use a vector quantization based approach which allows continuous embeddings to be associated with each latent variable value . |
| Outcome: | The proposed model outperforms a language modeling-based method on several tasks and lowers perplexity scores. |
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)
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| Challenge: | a learner language (interlanguage) is an idiolect developed by a learning of a second or foreign language. |
| Approach: | They propose to use semantic role labeling as a case task to parse interlanguages . they then evaluate three off-the-shelf SRL systems to gauge how successful they are . |
| Outcome: | The proposed model achieves an F-score of 72.06, a 2.02 point improvement over the baseline. |
A Teacher-Student Framework for Maintainable Dialog Manager (D18-1)
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| Challenge: | Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems . but extending RL-based systems to handle new intents and slots requires a system redesign . |
| Approach: | They propose a teacher-student framework to extend RL-based dialog systems . they propose to specify constraints held in the new dialog manager . |
| Outcome: | The proposed framework makes no assumption about unsupported intents and slots, making it possible to improve RL-based systems incrementally. |
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning (D18-1)
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| Challenge: | Existing approaches to improve the effectiveness and robustness of Deep Dyna-Q (DDQ) are based on a discriminator to control the quality of simulated experiences and to improve learning. |
| Approach: | They propose to use an RNN-based discriminator to control the quality of simulated experience to improve the effectiveness and robustness of Deep Dyna-Q. |
| Outcome: | The proposed framework outperforms DDQ by controlling the quality of simulated experience used for planning. |
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding (D18-1)
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| Challenge: | Spoken language understanding (SLU) involves intent determination and slot filling . existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. |
| Approach: | They propose a self-attentive model to fully utilize the semantic correlation between slot and intent. |
| Outcome: | The proposed model outperforms existing methods in both intent detection and slot filling tasks on ATIS benchmarks. |
Learning End-to-End Goal-Oriented Dialog with Multiple Answers (D18-1)
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| Challenge: | Existing methods for dialog learning assume there is only one correct next utterance . a significant drop in performance is seen in existing methods for evaluating dialog systems . |
| Approach: | They propose a method that assumes there is only one correct next utterance in a dialog . they propose bAbI dialog tasks that introduce valid next . |
| Outcome: | The proposed method improves performance and achieves 47.3% accuracy on permuted-bAbI dialog tasks. |
AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)
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| Challenge: | Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated. |
| Approach: | They propose to use a context-generator to generate travel and flight restrictions to train agents. |
| Outcome: | The proposed model achieves a score of 0.17 while humans can reach 0.91 . the proposed model is based on a large dataset that contains 301,427 goal-oriented conversations . |
QuaSE: Sequence Editing under Quantifiable Guidance (D18-1)
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| Challenge: | Existing methods for Quantifiable Sequence Editing (QuaSE) require editing an input sequence to generate an output that satisfies a numerical outcome value measuring a certain property of the sequence. |
| Approach: | They propose a framework for Quantifiable Sequence Editing that allows editing an input sequence to change an outcome and keep the content. |
| Outcome: | The proposed framework disentangles outcome factor and content factor from the input sentence to allow editing to change the outcome and keep the content. |
Paraphrase Generation with Deep Reinforcement Learning (D18-1)
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| Challenge: | Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT) |
| Approach: | They propose a deep reinforcement learning approach to automatic paraphrase generation . they propose supervised learning and reinforcement learning for evaluators . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in paraphrase generation on two datasets. |
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)
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| Challenge: | Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data. |
| Approach: | They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data. |
| Outcome: | The proposed framework improves the fidelity of the generated texts to the input structured data. |
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)
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| Challenge: | Existing models for automatic poetry generation lack term novelty and thematic consistency. |
| Approach: | They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation. |
| Outcome: | The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles. |
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (D18-1)
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| Challenge: | Existing rule-based question generation models rely on one or two sentences as input, while long text has posed challenges for sequence to sequence neural models. |
| Approach: | They propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. |
| Outcome: | The proposed model outperforms existing models with sentence-level or paragraph-level inputs pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4). |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
Unsupervised Natural Language Generation with Denoising Autoencoders (D18-1)
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| Challenge: | Unsupervised approaches to generating text from structured data are costly to obtain and limited to a limited domain. |
| Approach: | They propose an unsupervised approach that learns its parameters without the slot pairs on target sequences only. |
| Outcome: | The proposed approach can generate sentences out of corrupted data without supervision . it can be used in question answering and dialog systems, the authors show . |
Answer-focused and Position-aware Neural Question Generation (D18-1)
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| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
| Approach: | They propose an answer-focused and position-aware neural question generation model to address these issues. |
| Outcome: | The proposed model outperforms the baseline and outperformed the state-of-the-art system. |
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation (D18-1)
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| Challenge: | Existing text generation methods tend to produce repeated and ”boring” expressions. |
| Approach: | They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem. |
| Outcome: | The proposed model generates more diverse and informative text than existing baselines on review generation and dialogue generation tasks. |
Towards a Better Metric for Evaluating Question Generation Systems (D18-1)
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| Challenge: | Existing evaluation metrics based on n-gram similarity do not correlate well with human judgments . large datasets for document Question Answering (QA) have enabled the development of end-to-end supervised models . |
| Approach: | They propose a scoring function to capture answerability of questions . they also integrate existing similarity metrics into the scoring function . |
| Outcome: | The proposed scoring function improves human judgments on question answerability . the proposed scoring functions are made publicly available . |
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
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| Challenge: | Automatic Chinese poetry generation is one of the first attempts towards computer writing. |
| Approach: | They propose a model which requires no supervised style labeling to generate stylistic poems . they incorporate mutual information, a concept in information theory, into modeling . |
| Outcome: | The proposed model generates stylistic poems without losing fluency and coherency . it is based on mutual information, a concept in information theory . |
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (D18-1)
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| Challenge: | Neural conversation models tend to generate safe, generic responses for most inputs . this is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. |
| Approach: | They propose a distributional constraint approach that incorporates side information into the generated responses. |
| Outcome: | The proposed approach generates responses that are less generic without sacrificing plausibility. |
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
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| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |
Incorporating Background Knowledge into Video Description Generation (D18-1)
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| Challenge: | Existing methods for video captioning focus on generating generic descriptions that lack contextual knowledge. |
| Approach: | They propose a method that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. |
| Outcome: | The proposed model is based on a news video dataset and is evaluated on it. |
Multimodal Differential Network for Visual Question Generation (D18-1)
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| Challenge: | Current dialog systems show improvement in visual question answering but this does not translate to improved human-AI dialog. |
| Approach: | They propose to use a Multimodal Differential Network to generate natural questions from images using a multimodal differential network. |
| Outcome: | The proposed approach significantly improves over state-of-the-art benchmarks on the quantitative metrics. |
Entity-aware Image Caption Generation (D18-1)
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| Challenge: | Existing image captioning approaches generate generic descriptions of visual content and ignore background information. |
| Approach: | They propose a task which generates informative image captions using images and hashtags as input. |
| Outcome: | The proposed model outperforms unimodal baselines significantly with evaluation metrics on a dataset from Flickr. |
Learning to Describe Differences Between Pairs of Similar Images (D18-1)
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| Challenge: | Using crowd-sourced annotations, we generate text descriptions of differences between two images . we use a dataset to generate concise and fluent descriptions of visual data . |
| Approach: | They propose a task of automatically generating text to describe the differences between two images . they crowd-sourced the difference descriptions for pairs of images extracted from video-surveillance footage . |
| Outcome: | The proposed model outperforms models that use attention alone for single-sentence generation and multi-sentent generation. |
Object Hallucination in Image Captioning (D18-1)
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| Challenge: | Existing image captioning metrics do not capture image relevance . current metrics only measure similarity to ground truth captions . |
| Approach: | They propose a new image relevance metric to evaluate captioning models with veridical visual labels and assess their rate of object hallucination. |
| Outcome: | The proposed metrics show that models with veridical visual labels have higher hallucination rates than models with lower hallucinosity. |
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN (D18-1)
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| Challenge: | Recent research shows the strength of the Encoder-Decoder model in text summarization. |
| Approach: | They propose to use the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously and then to align the sentences and images in summaries. |
| Outcome: | The proposed model outperforms the existing methods that do not consider images . it can generate informative summaries of images, and it can be used to summarize documents . |
Keyphrase Generation with Correlation Constraints (D18-1)
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| Challenge: | Existing methods for keyphrase generation ignore correlation among keyphrases, resulting in duplication and coverage issues. |
| Approach: | They propose a new sequence-to-sequence architecture for keyphrase generation that captures correlation among keyphrases by preceding phrases to eliminate duplicate phrases and improve result coherence. |
| Outcome: | The proposed model outperforms the state-of-the-art method on benchmark datasets in terms of accuracy and diversity. |
Closed-Book Training to Improve Summarization Encoder Memory (D18-1)
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| Challenge: | Extractive summarization models pick words, phrases, and sentences from the source text to form a summary, while abstractive models generate (samples) words from a fixed-size vocabulary instead of copying from text directly. |
| Approach: | They propose to add an additional ‘closed-book’ decoder without attention and pointer mechanisms to the model to improve its memorization capabilities. |
| Outcome: | The proposed model outperforms the baseline model on the CNN/Daily Mail dataset in terms of ROUGE and METEOR metrics, and achieves higher scores in a test-only setup. |
Improving Neural Abstractive Document Summarization with Structural Regularization (D18-1)
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| Challenge: | Recent advances in document summarization fail to capture long-term structure of documents and multi-sentence summaries, resulting in information loss and repetitions. |
| Approach: | They propose to leverage structural information of documents and multi-sentence summaries to improve document summarization performance. |
| Outcome: | The proposed model outperforms state-of-the-art models on document summarization tasks. |
Iterative Document Representation Learning Towards Summarization with Polishing (D18-1)
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| Challenge: | Existing summarization methods read through document only once to generate a document representation, resulting in a sub-optimal representation. |
| Approach: | They propose an iterative model for supervised extractive text summarization which polishes the document representation on many passes through the document. |
| Outcome: | The proposed model outperforms state-of-the-art extractive systems on CNN/DailyMail and DUC2002 datasets. |
Bottom-Up Abstractive Summarization (D18-1)
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| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
| Approach: | They propose to use content selectors to over-determine phrases in a source document that should be part of the summary. |
| Outcome: | The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries. |
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning (D18-1)
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| Challenge: | Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. |
| Approach: | They propose a method which learns from users’ preferences instead of reference summaries by interacting with the oracle for multiple rounds and leveraging active learning, preference learning and reinforcement learning techniques. |
| Outcome: | The proposed method significantly advances the state of the art in both simulation and real-user experiments. |
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (D18-1)
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
| Approach: | They propose to use a maximal marginal relevance method to select representative sentences from multi-document input and leverage an abstractive encoder-decoder model to fuse disparate sentences to an abstract. |
| Outcome: | The proposed method compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |
MSMO: Multimodal Summarization with Multimodal Output (D18-1)
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| Challenge: | Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality. |
| Approach: | They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs. |
| Outcome: | The proposed method improves user satisfaction by 12.4% compared to the current system . |
Frustratingly Easy Model Ensemble for Abstractive Summarization (D18-1)
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| Challenge: | Existing studies on compressing or distilling ensemble models have shown that they increase computational costs and reduce performance. |
| Approach: | They propose an unsupervised method that combines multiple models by selecting a majority-like output in post-processing. |
| Outcome: | The proposed method performs better than the current ensemble methods on a news-headline-generation task. |
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries (D18-1)
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| Challenge: | Existing methods for evaluating content are not accurate because they only confirm if the summary contains small textual fragments. |
| Approach: | They propose to transform human-made reference summaries into extractive reference sums and weight them using elementary discourse units. |
| Outcome: | The proposed method strongly correlates with manual evaluations on DUC and TAC data sets. |
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (D18-1)
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| Challenge: | a popular approach to learning data representations involves the use of an auto-encoder that compresses data into a latent-space representation without supervision. |
| Approach: | They propose to train an auto-encoder that encodes input text into human-readable sentences . they use comprehensible natural language as a latent representation of the input source text . |
| Outcome: | The proposed auto-encoder can encode input text into human-readable sentences without document-summary pairs. |
Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings (D18-1)
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| Challenge: | Stack-Overflow, Quora, and Yahoo! Answers forums are not moderated, which results in noisy and redundant content. |
| Approach: | They use deep neural networks to learn meaningful task-specific embeddings . they incorporate the embeddables into a conditional random field model . |
| Outcome: | The proposed task improves significantly across evaluation metrics. |
What Makes Reading Comprehension Questions Easier? (D18-1)
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| Challenge: | Recent studies have shown that questions require a deeper understanding of language to answer beyond using superficial cues. |
| Approach: | They propose to use simple heuristics to split MRC datasets into easy and hard subsets and manually annotate questions from each subset with validity and reasoning skills to investigate which skills explain the difference between easy and harder questions. |
| Outcome: | The proposed model performs better for hard and easy questions than for easy questions. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)
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| Challenge: | Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone. |
| Approach: | They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations. |
| Outcome: | The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus. |
A Nil-Aware Answer Extraction Framework for Question Answering (D18-1)
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| Challenge: | Recent research suggests that reading comprehension-based question answering systems assume that every question has a valid answer in the associated passage. |
| Approach: | They propose a novel nil-aware answer span extraction framework that can return Nil or a text span from the associated passage as an answer in a single step. |
| Outcome: | The proposed framework outperforms baseline approaches on a newsQA dataset. |
Exploiting Deep Representations for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers. |
| Approach: | They propose to expose all of these signals with layer aggregation and multi-layer attention mechanisms and introduce an auxiliary regularization term to encourage different layers to capture diverse information. |
| Outcome: | The proposed approach exposes all of these signals with layer aggregation and multi-layer attention mechanisms on widely-used translation datasets. |
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures (D18-1)
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| Challenge: | Recent studies show that non-recurrent architectures outperform RNNs in neural machine translation. |
| Approach: | They hypothesize that CNNs and self-attentional networks could extract semantic features from source text. |
| Outcome: | The proposed architectures outperform RNNs on two tasks: subject-verb agreement and word sense disambiguation. |
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks (D18-1)
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| Challenge: | Existing gated recurrent networks have a vanishing gradient, allowing for more matrix transformations and less transparent functions. |
| Approach: | They propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. |
| Outcome: | The proposed system is more transparent than LSTM/GRU due to the simplification. |
Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)
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| Challenge: | Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study . |
| Approach: | They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up. |
| Outcome: | The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search. |
Revisiting Character-Based Neural Machine Translation with Capacity and Compression (D18-1)
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| Challenge: | Translating characters instead of words or word-fragments can simplify the processing pipeline but results in longer sequences . |
| Approach: | They propose to use sequence-to-sequence architectures of sufficient depth to solve the problem . they also evaluate the performance versus computation time tradeoffs they offer . |
| Outcome: | The proposed models outperform models operating over word fragments in character-level NMT, the authors show . they also show that the proposed models do not match the performance of their deep character baseline model . |
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation (D18-1)
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| Challenge: | Existing models for narrative story generation lack semantic dependency among sentences. |
| Approach: | They propose a skeleton-based model that generates the most critical phrases and expands them to a complete sentence. |
| Outcome: | The proposed model can generate significantly more coherent stories according to human evaluation and automatic evaluation. |
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation (D18-1)
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| Challenge: | Sequence-to-Sequence models favor short generic responses . however, the model is not suitable for modeling dialogues . |
| Approach: | They propose a model that connects preceding and following conversations to a prior distribution to avoid non-differentiability of discrete natural language tokens. |
| Outcome: | The proposed model is highly efficient in learning the backbone of human-computer communications, but favors short generic responses. |
A Neural Local Coherence Model for Text Quality Assessment (D18-1)
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| Challenge: | Existing approaches to local coherence modeling capture text relatedness at the level of sentence-to-sentence transitions. |
| Approach: | They propose a local coherence model that captures the flow of what connects adjacent sentences . they represent the semantics of a sentence by a vector and capture its state at each word . |
| Outcome: | The proposed model is beneficial for readability assessment and essay scoring tasks. |
Deep Attentive Sentence Ordering Network (D18-1)
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| Challenge: | Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific. |
| Approach: | They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Sentence Ordering and Order Discrimination tasks and is shown to be highly efficient. |
Getting to “Hearer-old”: Charting Referring Expressions Across Time (D18-1)
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| Challenge: | a study of how expressions that refer to an entity develop over time is done . we track thousands of person and organization entities over 20 years of NYT . |
| Approach: | They track thousands of person and organization entities over 20 years of the NYT . they find that referring expressions evolve over time as entities move from hearer-new to hearer old . |
| Outcome: | The proposed model improves on a majority-class baseline by 10-30% . it shows that the expressions evolve as the entity becomes accepted into common knowledge . |
Making “fetch” happen: The influence of social and linguistic context on nonstandard word growth and decline (D18-1)
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| Challenge: | In an online community, new words come and go, but language change is shaped and constrained by the grammatical system in which it takes part. |
| Approach: | They analysed the frequency of non-standard words in reddit to determine their impact on language change. |
| Outcome: | The results show that language change is shaped and constrained by the grammatical system in which it takes place. |
Analyzing Correlated Evolution of Multiple Features Using Latent Representations (D18-1)
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| Challenge: | phylogenetic models allow quantitative analysis of evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. |
| Approach: | They propose a latent representation-based analysis where discrete features are projected to a sequence of independent binary variables and phylogenetic inference is performed on the latent space. |
| Outcome: | The proposed model shows that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features. |
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting (D18-1)
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| Challenge: | Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. |
| Approach: | They use a corpus of 16.8M anonymous online posts to learn continuous document representations of cities. |
| Outcome: | The proposed method matches dialect areas at different granularities against an existing dialect map. |
Characterizing Interactions and Relationships between People (D18-1)
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| Challenge: | Existing methods to characterize the association between two people do not account for nuances in the relationship between two individuals. |
| Approach: | They propose to use a set of dimensions to characterize the association between two people. |
| Outcome: | The proposed model can be automated using dialogue scripts from the TV show Friends. |
Why Swear? Analyzing and Inferring the Intentions of Vulgar Expressions (D18-1)
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| Challenge: | Vulgar words are employed in language use for several different functions, including expressing aggression, signaling group identity or the informality of the communication. |
| Approach: | They present a dataset of 7,800 tweets with six categories of vulgarity in which all instances of vulgar words are annotated with one of the six categories. |
| Outcome: | The proposed model can predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. |
Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks (D18-1)
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| Challenge: | Activation functions are nonlinearities which have been attributed to the success story of deep learning. |
| Approach: | They propose to use a penalized tanh function to replace the sigmoid and tansh gates in LSTM cells and to improve the performance of the activation function. |
| Outcome: | The proposed activation function performs best on all tasks and can replace the sigmoid and tanh gates in LSTM cells. |
Hard Non-Monotonic Attention for Character-Level Transduction (D18-1)
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| Challenge: | Character-level string-to-string transductions are an important component of NLP tasks . hard non-monotonic attention models have been used for sequence modeling tasks involving characters . |
| Approach: | They propose an exact algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings. |
| Outcome: | The proposed algorithm outperforms soft attention and improves performance over stochastic approximation. |
Speed Reading: Learning to Read ForBackward via Shuttle (D18-1)
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| Challenge: | LSTM-Shuttle uses human speed reading techniques to perform natural language processing tasks. |
| Approach: | They propose a model which uses human speed reading techniques to perform natural language processing tasks for accurate and efficient comprehension. |
| Outcome: | The proposed model predicts on IMDB, Rotten Tomatoes, AG, and Children’s Book Test datasets and goes backwards. |
Modeling Localness for Self-Attention Networks (D18-1)
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| Challenge: | Existing approaches to model locality for self-attention networks have shown great value for capturing global dependencies. |
| Approach: | They propose to model localness for self-attention networks to capture local context . they cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. |
| Outcome: | The proposed model improves the ability to capture local context and improves accuracy. |
Chargrid: Towards Understanding 2D Documents (D18-1)
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Anoop R Katti, Christian Reisswig, Cordula Guder, Sebastian Brarda, Steffen Bickel, Johannes Höhne, Jean Baptiste Faddoul
| Challenge: | a novel type of text representation preserves the 2D layout of a document . a computer vision algorithm that extracts text from image is not optimal for understanding semantics . |
| Approach: | They propose a new type of text representation that preserves the 2D layout of a document . they demonstrate that it significantly outperforms approaches based on sequential text or document images . |
| Outcome: | The proposed approach outperforms approaches based on sequential text or document images on an information extraction task from invoices. |
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)
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| Challenge: | recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. |
| Approach: | They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation. |
| Outcome: | The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models. |
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)
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| Challenge: | Existing neural IR models do not have a mechanism for treating expansion terms differently from the original query terms, making it difficult to combine them with existing PRF approaches. |
| Approach: | They propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. |
| Outcome: | Extensive experiments on two standard test collections confirm the effectiveness of the proposed framework in improving the performance of two state-of-the-art neural IR models. |
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences (D18-1)
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| Challenge: | a long standing problem in NLP research is learning a matching function between two text sequences . a deep architecture for this task is proposed by a team of researchers . |
| Approach: | They propose a new deep matching model using stacked recurrent encoders to learn affinity weights . they conduct extensive experiments on six well-studied text sequence matching datasets a plethora of applications are possible . |
| Outcome: | The proposed model improves performance on six well-studied text sequence matching datasets. |
Spherical Latent Spaces for Stable Variational Autoencoders (D18-1)
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| Challenge: | Variational autoencoders use a multivariate Gaussian latent variable to capture latent structure in data. |
| Approach: | They propose a variational autoencoder which uses a latent distribution instead of Gaussian . they find that the variational posterior averts the KL collapse by a fixed hyperparameter . |
| Outcome: | The von Mises-Fisher distribution averts the KL collapse and gives better likelihoods than Gaussian models across a range of modeling conditions. |
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder (D18-1)
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| Challenge: | Existing methods to learn universal sentence representations focus on supervised learning. |
| Approach: | They propose a mean-max attention autoencoder that uses a multi-head mechanism to reconstruct the input sequence. |
| Outcome: | The proposed model outperforms state-of-the-art unsupervised single methods on a wide range of 10 transfer tasks. |
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)
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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
| Challenge: | Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy. |
| Approach: | They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings. |
| Outcome: | The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks. |
Multilingual Clustering of Streaming News (D18-1)
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| Challenge: | a novel method for clustering news across languages is proposed . a key challenge in handling news streams is that they must be generated on the fly . |
| Approach: | They propose a method for clustering news across languages into monolingual and crosslingual clusters . they use real news datasets in multiple languages to find an ever growing number of cluster labels . |
| Outcome: | The proposed method produces state-of-the-art results on real news datasets in German, English and Spanish. |
Multi-Task Label Embedding for Text Classification (D18-1)
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| Challenge: | Existing work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. |
| Approach: | They propose to combine multi-task learning with semantic vectors to convert labels into vectors . their results are based on extensive experiments on five benchmark datasets based in chinese . |
| Outcome: | The proposed model can improve performance on five benchmark datasets on text classification tasks. |
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)
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| Challenge: | a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text. |
| Approach: | They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit. |
| Outcome: | The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Uncertainty-aware generative models for inferring document class prevalence (D18-1)
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| Challenge: | Existing methods for inferring the relative frequency of classes of unlabeled examples are imperfect. |
| Approach: | They propose a generative probabilistic modeling approach to prevalence estimation . they back out an implicit individual-level likelihood function to conduct fast inference . |
| Outcome: | The proposed method provides better confidence interval coverage than an alternative and is significantly more robust to shifts in the class prior between training and testing. |
Challenges of Using Text Classifiers for Causal Inference (D18-1)
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| Challenge: | a number of scientific analyses focus on low-dimensional structured data, but text classifiers can be used to produce structured variables. |
| Approach: | They propose to use text classifiers to conduct causal analyses on simulated and Yelp data. |
| Outcome: | The proposed method can be used on simulated and Yelp data. |
Direct Output Connection for a High-Rank Language Model (D18-1)
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| Challenge: | Neural network language models have played a central role in recent natural language processing advances. |
| Approach: | They propose a recurrent neural network language model that combines probability distributions computed from a final RNN layer and also middle layers. |
| Outcome: | The proposed language model achieves the best score on the Penn Treebank and WikiText-2 benchmark datasets. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
Pyramidal Recurrent Unit for Language Modeling (D18-1)
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| Challenge: | Long short term memory units are powerful tools for language modeling, but their performance can be limited by the number of parameters. |
| Approach: | They propose a pyramidal recurrent unit which enables learning representations in high dimensional space with more generalization power and fewer parameters. |
| Outcome: | The proposed model outperforms existing models with different gating mechanisms and transformations on word-level language modeling tasks. |
On Tree-Based Neural Sentence Modeling (D18-1)
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| Challenge: | Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior. |
| Approach: | They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation . |
| Outcome: | The proposed tree-based sentences have shown better results on many downstream tasks. |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Siamese Network-Based Supervised Topic Modeling (D18-1)
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| Challenge: | Label-specific topics are widely used for supporting personality psychology, aspectlevel sentiment analysis, and crossdomain sentiment classification. |
| Approach: | They propose a supervised topic model based on the Siamese network which trades off label-specific word distributions with document-specific label distributions in a uniform framework. |
| Outcome: | The proposed model can trade off label-specific word distributions with document-specific label distributions in a uniform framework. |
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model (D18-1)
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| Challenge: | Conventional topic models suffer different problems in different settings. |
| Approach: | They propose a novel way to model word-pairs named biterms in the whole corpus and a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterm. |
| Outcome: | The proposed model generates more coherent topics compared with previous methods. |
Modeling Online Discourse with Coupled Distributed Topics (D18-1)
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| Challenge: | a topic model that incorporates structural relationships connecting documents in socially generated corpora is of limited application in the sciences. |
| Approach: | They propose a topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. |
| Outcome: | The proposed model captures discursive interactions along observed reply links and integrates latent distributed representations in a deep architecture. |
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts (D18-1)
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| Challenge: | a method for learning disentangled representations of texts that encode distinct and complementary aspects is proposed . a classic problem in distributed representation learning is that it is difficult to determine what information individual dimensions encode. |
| Approach: | They propose a method for learning disentangled representations of texts that encode distinct and complementary aspects by a adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. |
| Outcome: | The proposed method can be used to perform aspect-specific retrieval on biomedical abstracts. |
Multi-Source Domain Adaptation with Mixture of Experts (D18-1)
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| Challenge: | Existing methods for domain adaptation from multiple sources are designed to transfer supervision from a single source domain. |
| Approach: | They propose to capture the relationship between a target example and different source domains by a point-to-set metric. |
| Outcome: | The proposed method outperforms baselines and can handle negative transfer. |
A Neural Model of Adaptation in Reading (D18-1)
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| Challenge: | Several studies suggest that readers do adapt their lexical and syntactic predictions to the current context. |
| Approach: | They propose to add a simple adaptation mechanism to a neural language model to improve predictions of reading times. |
| Outcome: | The proposed model improves predictions of human reading times compared to a non-adaptive model. |
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study (D18-1)
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| Challenge: | Existing methods to evaluate deep learning models that are not considered for test set accuracy are difficult to interpret. |
| Approach: | They examine the impact of a test set question’s difficulty to determine if there is a relationship between difficulty and performance. |
| Outcome: | The proposed model can learn examples of varying difficulty at different rates if it does well on hard examples and poor on easy items because a dataset is all easy, but has "solved" anything? |
Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
| Outcome: | The proposed model makes systematic errors that are visible through the lens of factuality prediction. |
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models (D18-1)
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| Challenge: | In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) in neural languages modelling, called dual-FOFE. |
| Approach: | They propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) in neural languages modelling, called dual-FOFE. |
| Outcome: | The proposed method significantly reduces the complexity and improves perplexity by 10% over the original FOFE model. |
The Importance of Being Recurrent for Modeling Hierarchical Structure (D18-1)
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| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
| Approach: | They propose a convolutional sequence-to-sequence model that exploits hierarchical information implicitly. |
| Outcome: | The proposed model is recurrent and non-recurrent, and it can model hierarchical structure implicitly. |
Joint Learning for Targeted Sentiment Analysis (D18-1)
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| Challenge: | Recent studies have attempted to perform two tasks separately, e.g., target extraction and sentiment classification. |
| Approach: | They propose a hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model which allows the target label to influence their sentiment label. |
| Outcome: | The proposed model outperforms baseline models on two datasets and shows that it can learn abstract features. |
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification (D18-1)
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| Challenge: | Neural models that explicitly encode word order, syntax and semantic features are unequipped to deal with complex syntactic structures that affect sentiment, such as contrastive conjunctions. |
| Approach: | They propose to use contextualized ELMo embeddings instead of logic rules to compare models on syntactically complex sentences. |
| Outcome: | The proposed models outperform baseline models even on sentences with ambiguous sentiment labels. |
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)
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| Challenge: | Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. |
| Approach: | They propose a co-attention neural network model for emotion cause analysis with emotional context awareness. |
| Outcome: | The proposed model outperforms the state-of-the-art methods. |
Modeling Empathy and Distress in Reaction to News Stories (D18-1)
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| Challenge: | a recent work on empathy prediction has underestimated the complexity of the phenomenon and lacks a shared corpus. authors present a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. |
| Approach: | They propose a method which captures empathy assessments by the writer of a statement using multi-item scales. |
| Outcome: | The proposed method distinguishes between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
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| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models are usually trained with word-level loss under teacher forcing algorithm . however, this method suffers from exposure bias due to high variance of gradient estimation . |
| Approach: | They propose a method with a differentiable sequence-level training objective . they use greedy search to alleviate the problem of exposure bias . |
| Outcome: | Experiments on Chinese-to-English translation tasks show that the proposed method outperforms the reinforcement-based methods. |
Exploring Recombination for Efficient Decoding of Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) decoder captures features of entire prediction history . some partial hypotheses with different prefixes will be regarded differently no matter how similar they are . |
| Approach: | They propose a method that uses a n-gram suffix to adapt it to beam search decoding. |
| Outcome: | The proposed method can obtain similar translation quality with a smaller beam size, making it more efficient. |
Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation (D18-1)
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| Challenge: | Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. |
| Approach: | They empirically test neural machine translation on a Chinese–English news translation task . they show human raters prefer human over machine translation when evaluating documents . |
| Outcome: | The proposed method shows that human translators prefer document-level evaluation over machine translation . the results highlight the need to shift towards document- level evaluation as machine translation improves . |
Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point (D18-1)
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| Challenge: | a range of issues limit the performance of the automated metrics. |
| Approach: | They propose to use semi-automatic metrics and test suites instead of fully automatic metrics for pronoun translation. |
| Outcome: | The proposed metrics improve translation accuracy by comparing them against a manually annotated dataset . the proposed metrics are semi-automatic and test suites in place of fully automatic metrics. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
A strong baseline for question relevancy ranking (D18-1)
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| Challenge: | SemEval-16 and Semeval-17 community question answering shared tasks require complex pipelines and manual feature engineering to beat the IR baseline. |
| Approach: | They train a multi-task feed forward network on a bag of 14 distance measures for the input question pair and train it using language-independent features. |
| Outcome: | The proposed model outperforms the best shared task systems on the task of retrieving relevant previously asked questions. |
Learning Sequence Encoders for Temporal Knowledge Graph Completion (D18-1)
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| Challenge: | Existing work on link prediction in knowledge graphs has focused on static multi-relational data. |
| Approach: | They propose to learn latent entity and relation type representations to incorporate temporal information into knowledge graphs. |
| Outcome: | The proposed approach is robust to common challenges in real-world KGs. |
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (D18-1)
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| Challenge: | Event detection (ED) and word sense disambiguation (WSD) are similar tasks, but they require different neural representations. |
| Approach: | They propose a method to transfer the knowledge learned on WSD to ED by matching neural representations learned for the two tasks. |
| Outcome: | The proposed method can be applied to event detection and word sense disambiguation datasets. |
Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution (D18-1)
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| Challenge: | Existing word embedding models generate word representations by running long short-term memory recurrent neural networks on each sentence of an input article or conversation separately. |
| Approach: | They propose a word embedding model that learns cross-sentence dependency . they use linear sentence linking and attentional sentence linking to learn cross-entry dependency based on context sentences . |
| Outcome: | The proposed model improves end-to-end co-reference resolution by taking knowledge from context sentences and the entire document. |
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (D18-1)
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| Challenge: | Lexicon relation extraction given distributional representation of words is an important topic in NLP. |
| Approach: | They propose to use a word relation autoencoder to extract hypernyms from vocabularies . they propose to analyze the pollution and construct an indicator to measure it . |
| Outcome: | The proposed model outperforms the competitors on several hypernym-like lexicon datasets. |
Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation (D18-1)
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| Challenge: | Recent research trend is to refine or fine-tune pretrained word embeddings. |
| Approach: | They propose a method for refining pretrained word embeddings using layer-wise relevance propagation using a neural network. |
| Outcome: | The proposed method achieves higher performance than the original vectors. |
Learning Gender-Neutral Word Embeddings (D18-1)
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| Challenge: | Word embeddings trained on human-generated corpora inherit strong gender stereotypes . prior studies show such embeddables exhibit social biases, such as gender stereotype . |
| Approach: | They propose a method to preserve gender information in certain dimensions of word vectors . they propose GN-GloVe, which is a gender-neutral variant of the word embedding model . |
| Outcome: | The proposed method preserves gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. |
Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
| Challenge: | Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited. |
| Approach: | They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data. |
| Outcome: | The proposed approach obtains high correlation with human labels in the absence of labeled data. |
Word Sense Induction with Neural biLM and Symmetric Patterns (D18-1)
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| Challenge: | Existing methods for word sense induction use a language model to predict probable substitutes for target words. |
| Approach: | They propose to use a language model to predict probable substitutes for target words . they replace the ngram-based language model with a recurrent model to generate strong substitute vectors . |
| Outcome: | The proposed method surpasses the current state-of-the-art on the SemEval 2013 task by a large margin. |
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data (D18-1)
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| Challenge: | InferLite is a lightweight version of InferSent that does not use recurrent layers and can generalize to multiple pre-trained word embeddings. |
| Approach: | They propose a lightweight version of InferSent that does not use recurrent layers and operates on a collection of pre-trained word embeddings. |
| Outcome: | The proposed model outperforms existing models that learn generic embeddings in an unsupervised setting, often requiring several days or weeks to train. |
Similarity-Based Reconstruction Loss for Meaning Representation (D18-1)
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| Challenge: | a new paper proposes and evaluates a set of loss functions that can be used to train models for representation learning . cross-entropy loss penalizes models when they fail to generate the exact word from ground truth data . |
| Approach: | They propose and evaluate loss functions that can be used to train any neural model for representation learning. |
| Outcome: | The proposed loss functions amplify semantic diversity while preserving original meaning . they show performance improvement on paraphrase detection and language inference tasks . |
What can we learn from Semantic Tagging? (D18-1)
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
| Outcome: | The proposed model improves for part-of-speech tagging, universal dependency parsing and natural language inference. |
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop (D18-1)
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| Challenge: | Whether word's meaning varies across contexts has become a major focus of research in recent years. |
| Approach: | They propose a word embedding model that incorporates document covariates to estimate conditional word embeds. |
| Outcome: | The proposed model estimates word embedding distributions based on document covariates . if word embeds are statistically significant, hypothesis tests can be performed . |
Classifying Referential and Non-referential It Using Gaze (D18-1)
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| Challenge: | a particular problem for anaphora resolution systems is the pronoun it, which can be used both referentially and non-referentially. |
| Approach: | They use eye-tracking data to learn how humans perform disambiguation and use it to improve automatic classification. |
| Outcome: | The proposed system outperforms a baseline and outperformed linguistic-based approaches. |
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
Sanskrit Sandhi Splitting using seq2(seq)2 (D18-1)
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| Challenge: | Existing methods for word splitting in Sanskrit have low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. |
| Approach: | They propose a deep learning architecture called Double Decoder RNN which predicts the location of the splits with 95% accuracy and 79.5% accuracy. |
| Outcome: | The proposed model outperforms the state-of-the-art in the problem of Chinese word segmentation with 79.5% accuracy and the existing model's generalization capability. |
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling (D18-1)
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| Challenge: | Existing approaches to unsupervised Chinese word segmentation (CWS) are discriminative and generative, but they are non-trivial. |
| Approach: | They propose a neural generative model for fully unsupervised Chinese word segmentation (CWS) their approach explicitly focuses on the segmental nature of Chinese, and preserves several properties of language models. |
| Outcome: | The proposed model achieves competitive performance to the state-of-the-art models on four datasets from SIGHAN 2005 bakeoff. |
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
| Approach: | They propose a featureless neural network architecture that generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in Czech, German, and Arabic. |
Recovering Missing Characters in Old Hawaiian Writing (D18-1)
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| Challenge: | Modern Hawaiian orthography includes characters for long vowels and glottal stops . manual transliteration is laborious when performed manually . |
| Approach: | They propose two methods to help transliterate Hawaiian between older and newer texts automatically using finite state transducers and a recurrent neural network language model. |
| Outcome: | The proposed method solves the transliteration problem automatically using finite state transducers and a neural network language model. |
When data permutations are pathological: the case of neural natural language inference (D18-1)
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| Challenge: | Recent research indicates that these datasets are trivially annotated to a large degree. |
| Approach: | They propose to use a trending NLP task to test a neural architecture for NLI . they show a pathological sensitivity of the systems to permutations of the training set . |
| Outcome: | The proposed model outperforms the baseline model by multiple percentage points . the proposed model is based on the proposed dataset and the soundness of the task . |
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models (D18-1)
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| Challenge: | Textual entailment models focus on lexical gaps but rarely on knowledge gaps. |
| Approach: | They propose a fact-level decomposition of the hypothesis and a knowledge lookup module to fill knowledge gaps in Science Entailment task. |
| Outcome: | The proposed model outperforms the base model on the SciTail dataset by 3% and 5% on the textual premise and the structured knowledge base. |
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)
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| Challenge: | Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task. |
| Approach: | They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent. |
| Outcome: | The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm. |
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
| Approach: | They propose to interpret the intermediate layers of deep models by visualizing the saliency of attention and LSTM gating signals. |
| Outcome: | The proposed methods reveal interesting insights and identify critical information contributing to the model decisions. |
Towards Semi-Supervised Learning for Deep Semantic Role Labeling (D18-1)
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| Challenge: | Existing methods for semantic role labeling require an immense amount of semantic-role corpora and are therefore not suitable for low-resource languages or domains. |
| Approach: | They propose a semi-supervised method that outperforms the state-of-the-art on SRL . method explicitly enforcs syntactic constraints by augmenting the training objective with a syntastic-inconsistency loss component. |
| Outcome: | The proposed method outperforms the state-of-the-art on limited SRL training corpora on CoNLL-2012 English section. |
Identifying Domain Adjacent Instances for Semantic Parsers (D18-1)
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| Challenge: | Semantic parsers map text to logical forms, which can then be used by downstream components to fulfill an action. |
| Approach: | They propose a simple sentence representation that emphasizes unexpected words . they formalize domain-adjacency problem and propose logical form representations . |
| Outcome: | The proposed approach improves the performance of a downstream semantic parser on in-domain and domain-adjacent instances. |
Mapping natural language commands to web elements (D18-1)
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| Challenge: | a dataset of over 50,000 natural language commands captures various phenomena, including functional references, relational reasoning, and visual reasoning. |
| Approach: | They propose a task that requires the user to choose the correct element on a web page . they use a dataset of over 50,000 natural language commands to map these to web pages . |
| Outcome: | The proposed task can be viewed as a reference game based on a dataset of over 50,000 natural language commands . |
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (D18-1)
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| Challenge: | grammatical error correction is a labor-intensive task that requires large amounts of training data. |
| Approach: | They propose to use a human-annotated corpus of human-generated grammatical errors to generate a synthetic model. |
| Outcome: | The proposed method outperforms the current state of the art in grammatical error correction . human annotators achieve 39.39 F1 scores, suggesting the model generates mostly human-like instances . |
Modeling Input Uncertainty in Neural Network Dependency Parsing (D18-1)
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| Challenge: | Recent advances in neural network parsers address data sparsity issues by modeling character level information and exploiting raw data in semi-supervised settings. |
| Approach: | They investigate whether lexical normalization provides similar functionality to lexiconal normalization . they show that a separate normalization component improves performance of a neural network parser . |
| Outcome: | The proposed approaches improve performance even with access to character level information and word embeddings. |
Parameter sharing between dependency parsers for related languages (D18-1)
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| Challenge: | Using parameter sharing between parsers of related languages can improve performance, but there is no consensus on what parameters to share. |
| Approach: | They propose a model where transition classifier parameters are shared and word and character parameters are controlled by a parameter that can be tuned on validation data. |
| Outcome: | The proposed model improves on a monolingually trained baseline. |
Grammar Induction with Neural Language Models: An Unusual Replication (D18-1)
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| Challenge: | Recent work on latent tree learning attempts to develop models with parse-valued latent variables and train them on non-parsing tasks. |
| Approach: | They propose a model with parse-valued latent variables and a strong latent tree learning result on constituency parsing. |
| Outcome: | The proposed model outperforms all baselines and performs competitively with symbolic grammar induction systems. |
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages (D18-1)
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| Challenge: | Lack of sizable training datasets leads to poor performance in low-resource languages. |
| Approach: | They propose two techniques to augment training sets of low-resource languages using dependency trees. |
| Outcome: | The proposed methods improve on the training datasets for low-resource languages. |
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
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| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
| Outcome: | The proposed models perform on 14 out of 20 bAbI, SQuAD, CBT, CNN and Who-did-What datasets. |
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (D18-1)
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Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gašić
| Challenge: | a dataset of 10k human-human written conversations is one order of magnitude larger than previous annotated task-oriented corpora. |
| Approach: | They propose to collect 10k human-human written conversations from a crowd-sourced dataset using crowd-sourcing. |
| Outcome: | The proposed dataset is one order of magnitude larger than previous annotated task-oriented corpora and shows the usability of the data and sets a baseline for future studies. |
Linguistically-Informed Self-Attention for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models of semantic role labeling use no explicit linguistic features. prior work has shown that syntax trees can dramatically improve SRL decoding. |
| Approach: | They propose a neural network model that incorporates syntax using only raw tokens . they show that LISA out-performs the state-of-the-art with contextually-encoded word representations a 1.0 F1 on newswire and 2.0 F1 in out-of domain text . |
| Outcome: | The proposed model outperforms the state-of-the-art model with word embeddings and predicted predicates. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |