Proceedings of the 2018 Conference of the North
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)
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Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu
| Challenge: | NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation. |
| Approach: | They propose a label-aware double transfer learning framework for medical NER from electronic medical records. |
| Outcome: | The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks. |
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss (N18-1)
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| Challenge: | Existing methods for fine-grained type classification rely on distant supervision and are susceptible to noisy labels that can be out-of-context or overly-specific. |
| Approach: | They propose a neural network model that uses cross-entropy loss function to handle out-of-context labels and hierarchical loss normalization to cope with overly-specific ones. |
| Outcome: | The proposed model outperforms the state-of-the-art on established benchmarks for the task. |
Joint Bootstrapping Machines for High Confidence Relation Extraction (N18-1)
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| Challenge: | Existing semi-supervised bootstrapping methods for relationship extraction lack labeled data. |
| Approach: | They propose a semi-supervised bootstrapping method that protects against semantic drift . they expand entities and templates in parallel and in mutually constraining fashion in each iteration . |
| Outcome: | Experimental results show that BREX improves on state-of-the-art methods for four relationships. |
A Deep Generative Model of Vowel Formant Typology (N18-1)
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| Challenge: | a recent study has investigated the nature of vowel inventories, i.e., which vowels a language contains . a probabilistic approach does not rule out linguistic systems completely, but it can position phenomena on a scale from very common to very improbable. |
| Approach: | They propose a generative probability model of vowel inventory typology based on acoustic information rather than discrete symbols from the international phonetic alphabet. |
| Outcome: | The proposed model uses acoustic information rather than discrete symbols from the phonetic alphabet. |
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages (N18-1)
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| Challenge: | Morphological segmentation for polysynthetic languages is challenging because of limited training data. |
| Approach: | They propose two new multi-task training approaches that improve performance for Mexican polysynthetic languages . they also propose cross-lingual transfer as a third way to fortify their neural model . |
| Outcome: | The proposed models improve on Mexicanero, Nahuatl, Wixarika and Yorem Nokki . the proposed models reduce the amount of parameters by close to 75% . |
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
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| Challenge: | Morphologically complex words (MCWs) are multi-layer structures consisting of different subunits, each of which carries semantic information and has a specific syntactic role. |
| Approach: | They propose an extension to the state-of-the-art model which works at the character level and boosts the decoder with target-side morphological information. |
| Outcome: | The proposed model improves on the state-of-the-art model and can be extended to include morphologically complex words (MCWs) in three languages. |
Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information (N18-1)
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| Challenge: | acoustic signals provide cues that help listeners disambiguate difficult parses . speech carries useful extra information associated with prosodic structure . |
| Approach: | They propose a model that integrates transcribed text and acoustic-prosodic features into a neural network that accepts text and prosodic feature. |
| Outcome: | The proposed model improves parse and disfluency detection scores over a strong text-only baseline. |
Tied Multitask Learning for Neural Speech Translation (N18-1)
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| Challenge: | Recent efforts in endangered language documentation focus on collecting spoken language resources . BULB project uses mobile app to collect spoken resources accompanied by spoken translations . |
| Approach: | They propose a model where the second task decoder receives information from the first task . they apply regularization that encourages transitivity and invertibility to the model . |
| Outcome: | The proposed model improves performance on low-resource speech transcription and translation tasks. |
Please Clap: Modeling Applause in Campaign Speeches (N18-1)
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| Challenge: | a new corpus of speeches from campaign events is used to predict moments of audience applause . lexical features carry the most information, but a variety of features are predictive . |
| Approach: | They propose a corpus of speeches from campaign events in the months leading up to the 2016 election and develop new models for applause. |
| Outcome: | The proposed model predicts moments of audience applause from speeches at campaign rallies, rallies and rallies. |
Attentive Interaction Model: Modeling Changes in View in Argumentation (N18-1)
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| Challenge: | Prior work on argumentation in the NLP community has focused mainly on the first goal and has missed more nuanced and complex details of viewpoints. |
| Approach: | They propose a neural architecture that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument to predict if the argument succeeded in altering the OH' s view. |
| Outcome: | The proposed model outperforms several baselines on discussions on the Change My View forum on Reddit. |
Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data (N18-1)
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| Challenge: | Using focus-background dichotomy, discourse and information structure of sentences are being studied in context. |
| Approach: | They propose to automate the analysis of focus in authentic written data by using a range of lexical, syntactic, and semantic features to achieve an accuracy of 78.1%. |
| Outcome: | The proposed approach achieves 78.1% accuracy for identifying focus in authentic written data. |
Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)
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| Challenge: | a lack of training and evaluation datasets, benchmarks and automatic metrics has blocked progress in this field. |
| Approach: | They propose to use a grammarly's Yahoo Answers Formality corpus to create the largest corpus for a particular style . they also propose to apply machine translation metrics to the task . |
| Outcome: | The proposed model can be used to train and evaluate a text in a particular style . the proposed model is based on the existing model and can be applied to other tasks . |
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)
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| Challenge: | Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction. |
| Approach: | They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence. |
| Outcome: | The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB. |
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (N18-1)
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| Challenge: | a recent study has shown that natural language generators produce utterances with humanlike coherence and naturalness for many different kinds of content. |
| Approach: | They propose to use a neural language generator to generate a syntactically and semantically correct utterance from a given MR. |
| Outcome: | The proposed model outperforms state-of-the-art models on restaurant, TV and laptop datasets. |
A Melody-Conditioned Lyrics Language Model (N18-1)
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Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
| Challenge: | Existing models for lyrics generation are insufficient to capture relationship between lyrics and melody. |
| Approach: | They propose a data-driven language model that generates entire lyrics for a given melody. |
| Outcome: | The proposed model generates fluent lyrics while maintaining compatibility between lyrics and melodies. |
Discourse-Aware Neural Rewards for Coherent Text Generation (N18-1)
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| Challenge: | Existing approaches to train text generation models using cross-entropy loss do not always correlate well with achieving high scores on commonly used evaluation measures. |
| Approach: | They propose to use discourse-aware rewards to model cross-sentence ordering to approximate desired discourse structure to train a model of long, coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive text than models trained with cross-entropy or with commonly used scores as rewards. |
Natural Answer Generation with Heterogeneous Memory (N18-1)
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| Challenge: | Recent work on memory augmented encoder-decoder frameworks has shown promising progress for natural language generation tasks. |
| Approach: | They propose a memory-augmented encoder-decoder framework that takes care of memory contents from different sources to explicitly avoid repetition. |
| Outcome: | The proposed approach can produce readable and meaningful answer sentences while maintaining high coverage for given answer information. |
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation (N18-1)
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| Challenge: | Existing models tend to memorize words instead of learning meaning of words . existing models tend not to model semantic information, resulting in incorrect sentences . |
| Approach: | They propose a novel model that generates words by querying distributed word representations . they evaluate model on two paraphrase-oriented tasks, namely text simplification and short abstractive summarization . |
| Outcome: | The proposed model outperforms the baseline model on two paraphrase-oriented tasks . it achieves state-of-the-art performance on these benchmark datasets . |
Simplification Using Paraphrases and Context-Based Lexical Substitution (N18-1)
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| Challenge: | Lexical simplification involves identifying complex words or phrases that need to be simplified and suggesting simpler meaning-preserving substitutes. |
| Approach: | They propose a complex word identification model that exploits both lexical and contextual features and a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. |
| Outcome: | The proposed model detects complex words with higher accuracy than other models and proposes good substitutes in context. |
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (N18-1)
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| Challenge: | Existing factoid question answering systems rely on annotated datasets such as SimpleQuestions to generate questions from knowledge graphs. |
| Approach: | They propose a neural model that generates questions from knowledge graphs triples in a “zero-shot” setup. |
| Outcome: | The proposed model outperforms state-of-the-art on this task. |
Automated Essay Scoring in the Presence of Biased Ratings (N18-1)
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| Challenge: | Existing studies on rater effects in general settings have not investigated how rater bias affects automated essay scoring. |
| Approach: | They propose to model rater bias by removing essays associated with potentially biased scores from annotated corpus. |
| Outcome: | The proposed model is based on comments provided by raters and is compared with existing corpus. |
Content-Based Citation Recommendation (N18-1)
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| Challenge: | Existing citation recommendation systems rely on information of query documents such as author names and publication venue. |
| Approach: | They propose a content-based method for recommending citations in academic paper drafts . they embed a given query document into a vector space and use its nearest neighbors as candidates . |
| Outcome: | The proposed method outperforms published methods on PubMed and DBLP datasets without metadata. |
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (N18-1)
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| Challenge: | Using a dataset of 6,500+ questions, we found that human solvers achieved an F1-score of 88.1%. |
| Approach: | They propose a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. |
| Outcome: | The proposed reading comprehension challenge is based on a reading comprehension dataset with 6,500+ questions and 1000+ paragraphs across 7 domains. |
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)
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| Challenge: | Existing approaches to Automated Essay Scoring (AES) are not well-suited to capture adversarially crafted input of grammatical but incoherent sequences of sentences. |
| Approach: | They propose a neural model of local coherence that can effectively learn connectedness features between sentences. |
| Outcome: | The proposed approach strengthens the validity of neural essay scoring models. |
QuickEdit: Editing Text & Translations by Crossing Words Out (N18-1)
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| Challenge: | Using statistical learning, a computer can rephrase a sentence by only pointing at words that should be avoided. |
| Approach: | They propose a framework for computer-assisted text editing that relies on simple interactions between human editors and tokens. |
| Outcome: | The proposed framework allows to get substantial modifications to a sentence without human intervention. |
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction (N18-1)
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| Challenge: | Existing work on task extraction has focused on identifying tasks within a single session . but, we aim to identify tasks that span across multiple sessions. |
| Approach: | They propose to embed query words into query vectors to capture task semantics . they propose to use query vector embedding to predict whether a session is a part of a broader search task . |
| Outcome: | The proposed method improves task extraction efficiency over existing methods . it can predict whether a session is part of a broader complex search task . |
Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
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| Challenge: | Recent work has used attention weights to visualize the focus of neural models in input data. |
| Approach: | They propose to use attention-based visualization techniques to infer token-level labels from a network trained only on sentence-level labeling. |
| Outcome: | The proposed approach outperforms gradient-based methods on four datasets and is expected to outperfect supervised methods. |
Variable Typing: Assigning Meaning to Variables in Mathematical Text (N18-1)
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| Challenge: | Scientific documents rely on mathematics to communicate ideas and results . textual contexts are strong domainspecific conventions governing how content is presented . |
| Approach: | They introduce a task of assigning one mathematical type to each variable in a sentence . they also introduce 'variable typing' task that focuses on assignment of meaning to variables . |
| Outcome: | The proposed model is the best performing model on an extrinsic task, the authors show . their model is compared to a formula index only containing raw symbols . |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Comparing Constraints for Taxonomic Organization (N18-1)
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| Challenge: | Using general ontologies and domain-specific ontology, taxonomies encode knowledge that is important for understanding systems. |
| Approach: | They propose to modify a non-transitive branching algorithm to explicitly incorporate synonymy into the taxonomy structure to give it a faster performance. |
| Outcome: | The proposed method outperforms the best transitive algorithm while giving comparable performance over a dataset of local taxonomies. |
Improving Lexical Choice in Neural Machine Translation (N18-1)
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| Challenge: | False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words . |
| Approach: | They propose to fix the norms of both vectors to a constant value and integrate a lexical module which is jointly trained with the rest of the model. |
| Outcome: | The proposed approach achieves improvements of up to +4.3 BLEU surpassing phrase-based translation in nearly all settings. |
Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
Classical Structured Prediction Losses for Sequence to Sequence Learning (N18-1)
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| Challenge: | Recent work on training neural attention models at the sequence level has focused on a series of objective functions commonly used for structured prediction. |
| Approach: | They propose to use objective functions commonly used to train linear models for structured prediction to train neural attention models at the sequence-level using either reinforcement learning-style methods or beam search optimization. |
| Outcome: | The proposed model outperforms beam search optimization on German-English translation and abstractive summarization tasks. |
Deep Dirichlet Multinomial Regression (N18-1)
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| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)
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| Challenge: | Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences. |
| Approach: | They propose a method that captures conversational context and user content and behavior preferences. |
| Outcome: | The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets. |
Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation (N18-1)
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| Challenge: | Existing research lacks solid empirical investigation of typology of ad hominem arguments and their potential causes. |
| Approach: | They propose to perform several large-scale annotation studies and experiment with various neural architectures to validate hypotheses such as controversy or reasonableness. |
| Outcome: | The proposed model identifies the ad hominem fallacy and its possible causes using explainable neural network architectures. |
Scene Graph Parsing as Dependency Parsing (N18-1)
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| Challenge: | Recent studies have focused on parsing structured knowledge graphs from textual descriptions. |
| Approach: | They propose an alternative but equivalent scene graph representation that connects to dependency parses. |
| Outcome: | The proposed model outperforms best approaches on image retrieval applications. |
Learning Visually Grounded Sentence Representations (N18-1)
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
| Outcome: | The proposed model improves on word embeddings and word representations on standard benchmarks. |
Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision (N18-1)
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| Challenge: | Comparatives, vague quantification, and proportional estimation are not learned at the same time nor governed by the same rules during language acquisition. |
| Approach: | They propose to combine sets comparison, vague quantification, and proportional estimation to learn them together from visual scenes. |
| Outcome: | The proposed model can generalize to unseen combinations of target/non-target objects. |
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
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| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
Abstract Meaning Representation for Paraphrase Detection (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is ideal for paraphrase detection . it abstracts away from the syntactic realization of a sentence, and denotes only its meaning in a canonical form. |
| Approach: | They propose a technique that uses latent semantic analysis to translate sentences into AMR graphs . they show that the technique can be used to detect whether two sentences have the same meaning . |
| Outcome: | The proposed technique significantly advances state-of-the-art paraphrase detection for the Microsoft Research Paraphrase Corpus. |
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines (N18-1)
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| Challenge: | popular word embeddings are used to learn vector representations from the context of words. |
| Approach: | They propose a framework for jointly learning embeddings for words and contextual attributes based on factorization machines. |
| Outcome: | The proposed framework improves on a text classification task compared to learning embeddings independently. |
Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? (N18-1)
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| Challenge: | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks. |
| Approach: | They propose to embed a distributional thesaurus network into dense word vectors and compare them to state-of-the-art word representations. |
| Outcome: | The proposed representations improve performance against state-of-the-art word representations even without handcrafted lexical resources. |
Deep Neural Models of Semantic Shift (N18-1)
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| Challenge: | Diachronic distributional models track changes in word use over time using a continuous variable and a synthetic task to measure the semantic trajectory of a word. |
| Approach: | They propose a deep neural network diachronic distributional model that represents time as a continuous variable and model a word’s usage as . a synthetic task which measures how well a model captures the semantic trajectory of a . word over time. |
| Outcome: | The proposed model can capture the semantic trajectory of a word over time and can measure the speed of lexical change. |
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (N18-1)
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| Challenge: | Existing unsupervised methods for learning hypernyms from unlabeled text are not scaled to large vocabularies or yield unacceptably poor accuracy. |
| Approach: | They propose an unsupervised method of hypernym discovery using word contexts . they use word2vec to embed word context distributions without supervision . |
| Outcome: | The proposed method provides double the precision and highest average performance on 11 datasets. |
Mining Possessions: Existence, Type and Temporal Anchors (N18-1)
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| Challenge: | Existing annotations for possession relations can be used to predict possession existence, possession type and temporal anchors. |
| Approach: | They propose to use text annotations to mine possession relations from text . they assign temporal anchors indicating when possession holds between possessor and possessee . |
| Outcome: | The proposed task can predict possession existence, possession type and temporal anchors, and it can be automated. |
Neural Tensor Networks with Diagonal Slice Matrices (N18-1)
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| Challenge: | A large number of parameters can cause overfitting and a long training time for neural tensor networks (NTNs). |
| Approach: | They propose two new parameter reduction techniques to reduce the number of parameters in an NTN without diminishing its expressiveness. |
| Outcome: | The proposed models learn better and faster than the original (R)NTNs. |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources (N18-1)
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| Challenge: | Word vector specialisation is a portable, light-weight approach to fine-tuning distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. |
| Approach: | They propose a constraint-driven vector space specialisation method that embeds external knowledge into lexical resources into a deep neural network to specialise unseen words. |
| Outcome: | The proposed method preserves useful linguistic knowledge for seen words while propagating external signal to unseen words to improve their vector representations. |
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (N18-1)
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| Challenge: | Currently, unsupervised word embeddings are routinely trained on large amounts of raw text data. |
| Approach: | They propose to use unsupervised word embeddings to train distributed representations of sentences. |
| Outcome: | The proposed method outperforms state-of-the-art models on most benchmark tasks and is robust to the produced general-purpose sentence embeddings. |
Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
| Outcome: | The proposed model outperforms existing methods on multi-domain sentiment analysis significantly. |
Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | Existing work on tree structures uses syntactic parsers or Treebank annotations to perform target-dependent classifications. |
| Approach: | They propose a reinforcement learning based approach which automatically induces target-specific sentence representations over tree structures. |
| Outcome: | The proposed model gives superior performance on two benchmark tasks compared to previous work on parsed trees . |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
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| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality (N18-1)
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| Challenge: | Efficient word representations play an important role in solving various problems related to NLP, data mining, text mining etc. |
| Approach: | They propose to leverage bilingual word embeddings learned through a parallel corpus to minimize the effect of data sparsity. |
| Outcome: | The proposed model is tested against state-of-the-art methods in two experimental setups. |
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling (N18-1)
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| Challenge: | Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). |
| Approach: | They propose to use multi-task learning to improve Opinion Role Labeling by using a related task which has substantially more data. |
| Outcome: | The proposed model outperforms the state-of-the-art model for Opinion Role Labeling (ORL) with more data. |
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
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| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
| Approach: | They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. |
| Outcome: | The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. |
Robust Cross-Lingual Hypernymy Detection Using Dependency Context (N18-1)
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| Challenge: | Existing approaches to cross-lingual hypernymy detection are sparse and can be trained on related languages with negligible loss of performance. |
| Approach: | They propose a family of unsupervised approaches for cross-lingual hypernymy detection which learns sparse, bilingual word embeddings based on dependency contexts. |
| Outcome: | The proposed approach significantly improves performance on this task, compared to approaches based only on lexical context. |
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction (N18-1)
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| Challenge: | Existing grammar correction methods are limited in their ability to correct highly local errors . evaluators are unable to distinguish noisy examples from nonsynthesized ones . |
| Approach: | They propose to synthesize parallel data by noising a clean monolingual corpus . they propose to apply noise to the corpus to syntherize additional noisy examples . |
| Outcome: | The proposed method can produce almost as strong results as training with nonsynthesized data. |
Self-Training for Jointly Learning to Ask and Answer Questions (N18-1)
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| Challenge: | Existing methods for question answering and question generation are hard to obtain in many domains. |
| Approach: | They propose a method for jointly learning to ask and answer questions . they leverage unlabeled text along with labeled question answer pairs for learning . |
| Outcome: | The proposed method improves on four benchmark datasets on question answering and question generation tasks. |
The Web as a Knowledge-Base for Answering Complex Questions (N18-1)
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| Challenge: | Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. |
| Approach: | They propose to decompose complex questions into a sequence of simple questions and compute the final answer from the sequence of answers. |
| Outcome: | The proposed framework improves performance from 20.8 precision@1 to 27.5 precision@1. |
A Meaning-Based Statistical English Math Word Problem Solver (N18-1)
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| Challenge: | Experimental results show that the proposed approach understands the meaning of each quantity in the text more. |
| Approach: | They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands . |
| Outcome: | The proposed approach outperforms existing systems on benchmark and noisy datasets. |
Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates (N18-1)
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| Challenge: | Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future and affects personality, motivation, emotion, decision making and stress coping processes. |
| Approach: | They propose to use a minimally supervised method to classify tweets in one of three temporal categories, past, present, and future, and a deep bi-directional long-term memory (BLSTM) to measure correlation between sentiment view of temporal orientation and different psycho-demographic factors. |
| Outcome: | The proposed method achieves 78.27% accuracy on a manually created test set. |
Querying Word Embeddings for Similarity and Relatedness (N18-1)
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| Challenge: | Word2Vec embeddings have become popular representations of word meaning . similarity between two words is often assumed to be a direction-less measure, whereas relatedness is inherently directional. |
| Approach: | They propose to use word embeddings to predict asymmetric association between words from a dataset of production norms to generate thematically related words. |
| Outcome: | The proposed model predicts asymmetric association between words from a recently published dataset of production norms. |
Semantic Structural Evaluation for Text Simplification (N18-1)
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| Challenge: | Current measures for evaluating text simplification systems focus on lexical aspects, neglecting its structural aspects. |
| Approach: | They propose to use a reference-less automatic evaluation procedure to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. |
| Outcome: | The proposed measure has a significant correlation with human judgments and is highly comparable with existing measures. |
Entity Commonsense Representation for Neural Abstractive Summarization (N18-1)
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| Challenge: | Current ELS’s are not sufficiently effective, possibly introducing unresolved ambiguities and irrelevant entities. |
| Approach: | They propose an off-the-shelf entity linking system to extract linked entities and propose Entity2Topic (E2T) module attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary. |
| Outcome: | The proposed model improves the performance of the Gigaword and CNN summarization datasets by at least 2 ROUGE points. |
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies (N18-1)
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| Challenge: | a dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications . identifying large, high-quality resources for summarization has called for creative solutions in the past. |
| Approach: | They present a summarization dataset of 1.3 million articles and summaries written by newsrooms of 38 major news publications. |
| Outcome: | The summarization dataset shows high diversity of summarizing styles . authors train existing methods on the data to evaluate its utility and challenges. |
Polyglot Semantic Parsing in APIs (N18-1)
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| Challenge: | Existing approaches to semantic parsing work by training individual models for each available parallel dataset of text-meaning pairs. |
| Approach: | They propose a polyglot semantic translation approach that trains on multiple datasets and natural languages to learn parsing models. |
| Outcome: | The proposed model can be used for parsing a wide variety of natural languages and output languages, and achieves state-of-the-art performance on the above datasets. |
Neural Models of Factuality (N18-1)
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| Challenge: | A central function of natural language is to convey information about the properties of events. |
| Approach: | They propose to use a FactBank, UW, and MEANTIME event factuality dataset to build two neural models that outperform previous models. |
| Outcome: | The proposed models outperform previous models on FactBank, UW, and MEANTIME datasets. |
Accurate Text-Enhanced Knowledge Graph Representation Learning (N18-1)
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| Challenge: | Existing representation learning methods for knowledge graph representation do not consider the ambiguity of relations and entities. |
| Approach: | They propose a text-enhanced knowledge graph representation learning method which exploits the entity descriptions and triple-specific relation mention to enhance representations. |
| Outcome: | The proposed method outperforms existing representation learning models on link prediction and triple classification tasks and significantly outperformed existing models. |
Acquisition of Phrase Correspondences Using Natural Deduction Proofs (N18-1)
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| Challenge: | Existing methods for Recognizing Textual Entailment (RTE) lack phrasal knowledge. |
| Approach: | They propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. |
| Outcome: | The proposed method detects paraphrases that are absent from existing paraphrase databases and improves accuracy of RTE tasks. |
Automatic Stance Detection Using End-to-End Memory Networks (N18-1)
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| Challenge: | Existing methods for fact checking are tedious and often broken into intermediate steps to alleviate complexity. |
| Approach: | They propose an end-to-end memory network model that predicts whether a document can be considered relevant for a given claim and extracts relevant text snippets. |
| Outcome: | The proposed model predicts whether a document can be considered relevant for a given claim and extracts relevant text snippets to reason about the factuality of the target claim. |
Collective Entity Disambiguation with Structured Gradient Tree Boosting (N18-1)
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| Challenge: | Existing work on structured gradient tree boosting for collective entity disambiguation is limited to regular classification or regression problems. |
| Approach: | They propose a structured learning model that uses gradient tree boosting to disambiguate named entities in a document. |
| Outcome: | The proposed model outperforms the previous state-of-the-art neural system by near 1% absolute accuracy on the popular AIDA-CoNLL dataset. |
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors (N18-1)
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| Challenge: | Ontologies compartmentalize types and relations in a domain and require a process to establish alignments between entities to unify and extend existing knowledge. |
| Approach: | They propose a method which refines pre-trained word vectors to derivate ontological entity descriptions tailored to the ontology matching task. |
| Outcome: | The proposed method improves ontology matching performance over the current state-of-the-art. |
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
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| Challenge: | 185,445 claims generated by altering sentences from Wikipedia are verified without knowledge of the sentence they were derived from. |
| Approach: | They propose a publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
| Outcome: | The proposed dataset achieves 31.87% accuracy on labeling a claim accompanied by the correct evidence, compared to 50.91% if we ignore the evidence. |
Global Relation Embedding for Relation Extraction (N18-1)
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| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
| Approach: | They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision. |
| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
Implicit Argument Prediction with Event Knowledge (N18-1)
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| Challenge: | Existing work on identifying implicit arguments has been limited due to large number of features and small datasets . a neural model that uses narrative coherence and entity salience is used to train implicit arguments . |
| Approach: | They propose to train models for implicit argument prediction on a simple cloze task . they use narrative coherence and entity salience to build a neural model . |
| Outcome: | The proposed model performs better on synthetic and natural data. |
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (N18-1)
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| Challenge: | Existing temporal extraction systems that extract temporal relations can be improved by using a resource that provides prior knowledge of the temporal order that events usually follow. |
| Approach: | They propose to use a probabilistic knowledge base acquired in the news domain to extract temporal relations between events from the New York Times articles over a 20-year span. |
| Outcome: | The proposed system and resource are both publicly available. |
Multimodal Named Entity Recognition for Short Social Media Posts (N18-1)
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| Challenge: | Social media posts often contain inconsistent or incomplete syntax and lexical notations with limited textual contexts. |
| Approach: | They propose a task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data . they use a dataset called SnapCaptions to build upon the state-of-the-art NER models . |
| Outcome: | The proposed model outperforms existing models on noisy user-generated data . it uses a deep image network and generic modality attention module . |
Nested Named Entity Recognition Revisited (N18-1)
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| Challenge: | Existing methods for named entity recognition and mention detection ignore nested entities along with any semantic relations between them. |
| Approach: | They propose a recurrent neural network-based approach to handle named entity recognition and nested entity mention detection simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three standard datasets. |
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction (N18-1)
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| Challenge: | Existing work in relation extraction ignores relationships expressed across sentence boundaries . document-level annotation is common in biological text . |
| Approach: | They propose a model which simultaneously predicts relationships between all mention pairs in a document. |
| Outcome: | The proposed model is larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives. |
Supervised Open Information Extraction (N18-1)
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| Challenge: | Existing methods for Open Information Extraction (Open IE) use semisupervised approaches or rule-based algorithms. |
| Approach: | They propose a supervised approach to Open Information Extraction (Open IE) they build on recent deep Semantic Role Labeling models to extract Open IE tuples . |
| Outcome: | The proposed model outperforms state-of-the-art Open IE systems on benchmark datasets. |
Embedding Syntax and Semantics of Prepositions via Tensor Decomposition (N18-1)
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| Challenge: | Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe). |
| Approach: | They propose to use word-triple counts to capture a preposition’s interaction with its attachment and complement and derive preposition embeddings via tensor decomposition on a large unlabeled corpus. |
| Outcome: | The proposed model is comparable to or better than the state-of-the-art on multiple standardized datasets. |
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings (N18-1)
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| Challenge: | linguistic typology is the classification of languages according to their linguistic properties. |
| Approach: | They learn distributed language representations which can be used to predict typological properties on a massively multilingual scale. |
| Outcome: | The proposed model can predict typological properties on a massively multilingual scale. |
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses (N18-1)
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| Challenge: | Dependency parsing research focuses on improving accuracy of single-tree predictions . ambiguity is inherent to natural language syntax, and communicating it is important for error analysis . |
| Approach: | They propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a model and demonstrate its usefulness. |
| Outcome: | The proposed method can be used to propagate parse uncertainty to two downstream applications. |
Neural Particle Smoothing for Sampling from Conditional Sequence Models (N18-1)
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| Challenge: | In structured prediction problems, labeling an input string with a length-T sequence of tags becomes intractable. |
| Approach: | They propose a sequential Monte Carlo method for sampling annotations of an input string from a given probability model. |
| Outcome: | The proposed method improves the quality of the sample. |
Neural Syntactic Generative Models with Exact Marginalization (N18-1)
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| Challenge: | Recent models have added structure to recurrent neural networks at the cost of giving up exact inference, or using soft structure instead of latent variables. |
| Approach: | They propose a syntactic generative model with exact marginalization that supports dependency parsing and language modeling. |
| Outcome: | The proposed models achieve state-of-the-art for supervised dependency parsing and language modeling. |
Noise-Robust Morphological Disambiguation for Dialectal Arabic (N18-1)
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| Challenge: | Noisy content is non-canonical in nature, with lexical, orthographic, and phonetic variations. |
| Approach: | They propose a neural morphological tagging and disambiguation model for Egyptian Arabic with various extensions to handle noisy content. |
| Outcome: | The proposed model achieves about 5% relative error reduction over a state-of-the-art baseline for Egyptian Arabic. |
Parsing Tweets into Universal Dependencies (N18-1)
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| Challenge: | a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD). |
| Approach: | They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. |
| Outcome: | The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed. |
Robust Multilingual Part-of-Speech Tagging via Adversarial Training (N18-1)
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| Challenge: | Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. |
| Approach: | They propose and analyze a neural POS tagging model that exploits adversarial training by training on unmodified and adversarials. |
| Outcome: | The proposed model improves overall tagging accuracy and prevents over-fitting in low resource languages and boosts tabbing accuracy for rare / unseen words. |
Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)
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| Challenge: | Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed. |
| Approach: | They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data. |
| Outcome: | The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration. |
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
| Approach: | They exploit translation as a distributional context and embed words as posterior probability densities, rather than point estimates, which allows them to compare words in context using a measure of overlap between distributions. |
| Outcome: | The proposed model performs on a range of lexical semantics tasks and achieves competitive results on benchmarks including natural language inference, paraphrasing, and text similarity. |
Learning Word Embeddings for Low-Resource Languages by PU Learning (N18-1)
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| Challenge: | Existing approaches to learn word embedding on a corpus with only a few million tokens are limited to low-resource languages. |
| Approach: | They propose to use a sparse co-occurrence matrix to factorize the co-existence matrix and validate the proposed approaches in four different languages. |
| Outcome: | The proposed model is validated in four different languages. |
Exploring the Role of Prior Beliefs for Argument Persuasion (N18-1)
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| Challenge: | Recent studies in natural language processing (NLP) have shown that the language of opinion holders and their patterns of interaction play a key role in changing the mind of a reader. |
| Approach: | They propose to use a dataset to study the effect of language use vs. prior beliefs on persuasion in a controlled setting that takes into account political and religious ideology. |
| Outcome: | The proposed controlled setting takes into account political and religious ideology and shows that prior beliefs play a more important role than language use effects. |
Inducing a Lexicon of Abusive Words – a Feature-Based Approach (N18-1)
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| Challenge: | a new classification task is needed to identify abusive words among a set of negative polar expressions. |
| Approach: | They propose to calibrate a domain-independent lexicon for detection of abusive words . they use a small manually annotated base lexico to calibrated a large lexical . |
| Outcome: | The proposed feature can be calibrated on a small manually annotated base lexicon and produced on large datasets. |
Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions (N18-1)
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| Challenge: | Social power is a difficult concept to define, but is often manifested in how we interact with one another. |
| Approach: | They employ extra-propositional semantics extraction within NLP to study author commitment . they find that subordinates use significantly more instances of non-commitment than superiors . |
| Outcome: | The proposed method shows that subordinates use significantly more instances of non-commitment than superiors, and that enriching lexical features with commitment labels captures important distinctions in social meanings. |
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification (N18-1)
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| Challenge: | Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. |
| Approach: | They propose to use automatic word deletion to generate local explanations for a text classification model by crowdsourcing the evaluation using a crowdsourced experiment. |
| Outcome: | The proposed evaluations of local explanations using crowdsourcing and automatic measures correlate with the results. |
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time (N18-1)
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| Challenge: | a novel topic model is proposed to allow topical trends to be captured in temporal collections of documents. |
| Approach: | They propose a novel unsupervised neural dynamic topic model where topics are influenced by topic discovery over time. |
| Outcome: | The proposed model shows better generalization, topic interpretation, evolution and trends compared to state-of-the-art models . |
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation (N18-1)
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| Challenge: | Existing metrics to evaluate multilingual topic quality are inadequate for multilingual document analysis. |
| Approach: | They propose a new intrinsic evaluation metric for multilingual topic models that correlates well with human judgments of multilingual coherence and performance in downstream applications. |
| Outcome: | The proposed model improves the performance of multilingual topic models in low-resource languages and with human judgments of multilinguistic topic coherence. |
Explainable Prediction of Medical Codes from Clinical Text (N18-1)
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| Challenge: | Clinical notes are text documents that are created by clinicians for each patient encounter. |
| Approach: | They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. |
| Outcome: | The proposed method is accurate and better than the current state of the art. |
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (N18-1)
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| Challenge: | et al., 1996, show that many of the most actively studied problems in NLP depend in large part on natural language understanding (NLU). |
| Approach: | They propose a dataset for machine learning that uses ten different genres of English to evaluate sentences for their meanings. |
| Outcome: | The multi-genre natural language inference corpus is one of the largest available for natural language understanding. |
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations (N18-1)
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| Challenge: | Existing approaches to recognize lexical semantic relations between word pairs require that word pairs co-occur in a sentence. |
| Approach: | They propose to exploit lexico-syntactic paths between two target words to exploit the semantic relations between word pairs. |
| Outcome: | The proposed model can generalize the co-occurrences of word pairs and dependency paths and extract features capturing relational information from word pairs. |
Specialising Word Vectors for Lexical Entailment (N18-1)
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| Challenge: | Existing word representation learning methods rely on the distributional hypothesis to learn meaningful word representations. |
| Approach: | They propose a method that emphasises the asymmetric relation of lexical entailment by injecting external linguistic constraints into the input word vector space. |
| Outcome: | The proposed method achieves state-of-the-art in the tasks of hypernymy directionality, hypernomia detection, and graded lexical entailment. |
Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
| Outcome: | The proposed method overcomes structural differences between the target languages and requires no gold standard data. |
Sentences with Gapping: Parsing and Reconstructing Elided Predicates (N18-1)
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| Challenge: | Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments. |
| Approach: | They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. |
| Outcome: | The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)
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| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
| Outcome: | The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points. |
Colorless Green Recurrent Networks Dream Hierarchically (N18-1)
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| Challenge: | Recurrent neural networks (RNNs) can induce non-trivial properties of language. |
| Approach: | They investigate whether RNNs can track hierarchical syntactic structure . they include nonsensical sentences where RNN cannot rely on semantic cues . |
| Outcome: | The proposed models can predict long-distance agreement in nonsensical sentences in Italian and English. |
Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
| Challenge: | Existing methods for few-shot learning are insufficient to capture task variations in natural language domains. |
| Approach: | They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. |
| Outcome: | The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets. |
Early Text Classification Using Multi-Resolution Concept Representations (N18-1)
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| Challenge: | e-communications have been misused by cyber-criminals, who hide in the depths of the web. |
| Approach: | They propose a document representation which allows us to generate multiple "views" of the analyzed text. |
| Outcome: | The proposed representation outperforms existing models in two tasks where anticipation is critical: sexual predator detection and depression detection. |
Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)
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| Challenge: | Existing methods for text classification are domain-dependent, but there is no annotated data for some domains. |
| Approach: | They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task . |
| Outcome: | The proposed model outperforms the prior art on the multi-domain text classification task. |
Pivot Based Language Modeling for Improved Neural Domain Adaptation (N18-1)
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| Challenge: | Existing work on domain adaptation does not exploit the structure of the input text . PBLM can naturally feed structure aware text classifiers such as LSTM and CNN . |
| Approach: | They propose a model that integrates pivot-based and NN modeling in a structure aware manner. |
| Outcome: | The proposed model can naturally feed structure aware text classifiers such as LSTM and CNN. |
Reinforced Co-Training (N18-1)
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| Challenge: | Existing co-training methods ignore sampling bias and fail to explore the data space. |
| Approach: | They propose a method to select high-quality unlabeled samples to better co-train on by learning a selection policy with a small labeled dataset. |
| Outcome: | The proposed method can obtain more accurate text classification results on clickbait detection and generic text classification tasks. |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
The Context-Dependent Additive Recurrent Neural Net (N18-1)
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| Challenge: | Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). |
| Approach: | They propose a new family of Recurrent Neural Networks that address contextual sequence mapping . they propose to use contextual signals to control the flow of information . |
| Outcome: | The proposed architecture outperforms existing methods on dialog problem and language model . the proposed architectures are based on a novel family of recurrent neural networks . |
Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention (N18-1)
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| Challenge: | Neural machine translation models learn to map from source language sentences to target language sentences via continuous-space intermediate representations. |
| Approach: | They propose an encoder with character attention which augments the (sub)word-level representation with character-level information and a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively. |
| Outcome: | The proposed model outperforms the standard word-based model, subword-based models, and strong character-based ones on translation tasks. |
Dense Information Flow for Neural Machine Translation (N18-1)
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| Challenge: | Recent advances in deep neural networks have improved learning performance for NMT . Residual connections allow features from previous layers to be accumulated to the next layer easily. |
| Approach: | They propose a densely connected NMT architecture that can train more efficiently for NMT. |
| Outcome: | The proposed architecture improves learning performance and attention quality on multiple datasets. |
Evaluating Discourse Phenomena in Neural Machine Translation (N18-1)
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| Challenge: | Existing models for machine translation have been evaluated with standard automatic metrics, but are poorly adapted to evaluating discourse phenomena. |
| Approach: | They propose to use BLEU to train multi-encoder NMT models on English subtitles to test their ability to exploit previous source and target sentences. |
| Outcome: | The proposed multi-encoder models give limited improvements on the coreference and coherence tests. |
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (N18-1)
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| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
| Approach: | They propose an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. |
| Outcome: | The proposed algorithm can place constraints and improve results in simulated post-editing tasks. |
Guiding Neural Machine Translation with Retrieved Translation Pieces (N18-1)
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| Challenge: | Neural machine translation (NMT) has trouble with lowfrequency words or phrases and generalizing across domains. |
| Approach: | They propose a method for recalling low-frequency words and phrases into neural machine translation by retrieving n-grams from a search engine and incorporating them into the decoding process. |
| Outcome: | The proposed method improves translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. |
Handling Homographs in Neural Machine Translation (N18-1)
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| Challenge: | Existing methods for MT have problems with translating homographs, as it is difficult to select the correct translation based on the context. |
| Approach: | They propose to model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. |
| Outcome: | The proposed models improve translation accuracy and BLEU score on three language pairs. |
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)
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| Challenge: | Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks. |
| Approach: | They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials. |
| Outcome: | The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks. |
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)
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| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
| Outcome: | The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese. |
Self-Attentive Residual Decoder for Neural Machine Translation (N18-1)
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| Challenge: | Neural sequence-to-sequence networks with attention have been used for machine translation . however, the target-side context is limited and the model lacks the ability to capture non-syntactic dependencies among words. |
| Approach: | They propose a sequence-to-sequence network with attention that captures contextual information at each time-step prediction through an attention mechanism. |
| Outcome: | The proposed model outperforms a neural MT baseline and memory and self-attention network on three language pairs. |
Target Foresight Based Attention for Neural Machine Translation (N18-1)
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| Challenge: | Empirical experiments on Chinese-to-English and Japanese-to English datasets show that the proposed attention model delivers significant improvements in terms of alignment error rate and BLEU. |
| Approach: | They propose to explicitly access the target foresight word in the attention model to improve alignment and translation accuracy. |
| Outcome: | Empirical results show that the proposed model improves alignment error rate and BLEU on Chinese-to-English and Japanese-toEnglish datasets. |
Context Sensitive Neural Lemmatization with Lematus (N18-1)
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| Challenge: | a new contextsensitive lemmatizer is designed to improve performance on unseen and ambiguous words. |
| Approach: | They propose a context-sensitive lemmatizer which incorporates character-level sentence context. |
| Outcome: | The proposed model outperforms the best competitor in a data setting and lower-resource setting . the model includes context for ambiguous words, but the latter has a greater effect on performance . |
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media (N18-1)
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| Challenge: | Current approaches to Named Entity Recognition (NER) are effective in formal text, but they fail on informal text, where improper grammatical structures, spelling inconsistencies, and slang vocabulary prevail. |
| Approach: | They propose a multitask end-to-end bidirectional long short-term memory (BLSTM)-Conditional Random Field (CRF) network with two CRF classifiers and a feature extractor that transfers learning to a CRF for prediction. |
| Outcome: | The proposed models outperform the current state-of-the-art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments. |
Reusing Weights in Subword-Aware Neural Language Models (N18-1)
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| Challenge: | a statistical language model assigns a probability to a sequence of words . data sparsity is a major problem in building traditional n-gram language models . |
| Approach: | They propose several ways to reuse subword embeddings and other weights in subword-aware neural language models. |
| Outcome: | The proposed techniques do not benefit a competitive character-aware model . but they show significant reductions in model sizes and performance. |
Simple Models for Word Formation in Slang (N18-1)
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| Challenge: | slang is a popular vocabulary among young people due to its extragrammatical properties and the rise of social media. |
| Approach: | They propose a data-driven approach coupled with linguistic knowledge to develop generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives. |
| Outcome: | The proposed models show that slang exhibits extragrammatical properties that distinguish it from the standard form. |
Using Morphological Knowledge in Open-Vocabulary Neural Language Models (N18-1)
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| Challenge: | Existing models that generate words from a fixed vocabulary are linguistically nave . authors present an open-vocabulary language model that incorporates morphological knowledge into a neural framework . |
| Approach: | They propose a model that incorporates morphological knowledge into a neural model by generating words as a sequence of characters, generating full word forms and combining them with a hand-written morphology analyzer. |
| Outcome: | The proposed model outperforms character-based models on Finnish, Turkish, and Russian on three languages. |
A Neural Layered Model for Nested Named Entity Recognition (N18-1)
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| Challenge: | Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language. |
| Approach: | They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory. |
| Outcome: | The proposed model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively in terms of F-score. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |
KBGAN: Adversarial Learning for Knowledge Graph Embeddings (N18-1)
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| Challenge: | Existing knowledge graph embedding techniques lack the capability to access similarities between entities and relations. |
| Approach: | They propose an adversarial learning framework to improve knowledge graph embedding models . they use one knowledge graph embedded model as a negative sample generator . |
| Outcome: | The proposed framework improves the performance of knowledge graph embedding models on a link prediction task. |
Multimodal Frame Identification with Multilingual Evaluation (N18-1)
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| Challenge: | FrameNet Semantic Role Labeling aims to disambiguate situations around predicates using textual representations. |
| Approach: | They extend a frame identification task to leverage multimodal representations to improve FrameNet Semantic Role Labeling. |
| Outcome: | The proposed system outperforms its unimodal counterpart on the English frameNet and its German counterpart on IMAGINED words. |
Learning Joint Semantic Parsers from Disjoint Data (N18-1)
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| Challenge: | Various formal meaning representations have been developed corresponding to different semantic theories. |
| Approach: | They propose a method to learn a semantic parser from multiple datasets by treating annotations for unobserved formalisms as latent structured variables. |
| Outcome: | The proposed approach improves on existing methods using unobserved formalisms and underlying corpora. |
Identifying Semantic Divergences in Parallel Text without Annotations (N18-1)
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| Challenge: | Parallel sentence pairs are sentences that are translations of each other and convey the same meaning in the source and target languages. |
| Approach: | They propose a model which detects meaning divergences in parallel sentence pairs . parallel sentence pair are translations of each other, therefore often assumed to convey the same meaning . |
| Outcome: | The proposed model detects divergences more accurately than models based on word alignments. |
Bootstrapping Generators from Noisy Data (N18-1)
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| Challenge: | Existing methods for data-to-text generation focus on learning correspondences between structured data and associated texts. |
| Approach: | They aim to bootstrap generators from large scale datasets where data and related texts are loosely aligned. |
| Outcome: | The proposed model improves on a vanilla encoder-decoder which relies on soft attention. |
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation (N18-1)
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| Challenge: | Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities. |
| Approach: | They propose a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. |
| Outcome: | The proposed models outperform models that encapsulate single-style or average-style language generation capabilities. |
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization (N18-1)
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| Challenge: | a proposed model for generating natural language descriptions is too generic and does not exploit task specific characteristics. |
| Approach: | They propose a model which uses a fused bifocal attention mechanism to exploit micro and macro level information and a gated orthogonalization mechanism to ensure that a field is remembered for a few time steps and then forgotten. |
| Outcome: | The proposed model improves on a recently released dataset with two similar datasets for French and German. |
CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension (N18-1)
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| Challenge: | Currently, machine comprehension datasets are extremely scarce for specialized domains. |
| Approach: | They propose a dataset for machine comprehension in the medical domain using clinical case reports with around 100,000 gap-filling queries about these cases. |
| Outcome: | The proposed dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. |
Learning to Collaborate for Question Answering and Asking (N18-1)
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| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering (N18-1)
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| Challenge: | Existing models for sentence pair ranking are based on hierarchical recurrent neural network and latent topic clustering module. |
| Approach: | They propose a hierarchical recurrent neural network and latent topic clustering module to adapt a recursive hierarchic neural network to rank candidate answers. |
| Outcome: | The proposed model shows small performance degradations in longer text comprehension compared to current models which suffer from it. |
Supervised and Unsupervised Transfer Learning for Question Answering (N18-1)
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| Challenge: | Several QA scenarios and datasets have been introduced over the past few years. |
| Approach: | They conduct extensive experiments to investigate the transferability of knowledge from a source QA dataset to a target dataset using two QA models. |
| Outcome: | The proposed model outperforms the previous best model on TOEFL listening comprehension test by 7% on target datasets. |
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension (N18-1)
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| Challenge: | Using synthetic data, existing models struggle with questions that require inference. |
| Approach: | They propose a dataset and two new neural models that exploit alternative mechanisms for state prediction. |
| Outcome: | The proposed dataset improves accuracy by 19% over previous models. |
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation (N18-1)
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| Challenge: | Existing models for user reviews are limited by data sparsity and lack of data. |
| Approach: | They propose to integrate LSTM and Topic Modeling to extract review information for recommender systems by utilizing user reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon review dataset and shows better ability on making topic clustering than traditional topic model based method. |
Deconfounded Lexicon Induction for Interpretable Social Science (N18-1)
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| Challenge: | Lexical features are useful beyond predictive performance. they can also be used to understand the subjective properties of a text. |
| Approach: | They propose two deep learning algorithms that separate the explanatory power of text from confounds. |
| Outcome: | The proposed algorithms are predictive of a set of target variables yet uncorrelated to confounds . they pick words associated with narrative persuasion and are more predictive than standard features . |
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security (N18-1)
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| Challenge: | Distributed Denial of Service (DDoS) attacks are becoming more frequent and more severe in their impact. |
| Approach: | They propose a feed-forward neural network and a partially labeled LDA model that use social media as an indirect measure of network service status. |
| Outcome: | The proposed model outperforms previous work by significant margins and provides the first fine-grained analysis of how the public reacts to ongoing network attacks. |
The Importance of Calibration for Estimating Proportions from Annotations (N18-1)
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| Challenge: | Existing approaches to measuring label proportions are based on an assumption which is invalid for many problems, especially when dealing with human annotations. |
| Approach: | They propose to use an annotation tool to estimate label proportions in a target corpus and to use it to validate the accuracy of the methods. |
| Outcome: | The proposed method emphasizes calibration and is validated against two data generating scenarios. |
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications (N18-1)
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Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
| Challenge: | a dataset of 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR is presented to study peer reviews. |
| Approach: | They propose to use the dataset to collect peer reviews from top-tier venues including ACL, NIPS and ICLR and to use it to create a dataset of peer reviews for research purposes. |
| Outcome: | The proposed dataset includes 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. |
Deep Communicating Agents for Abstractive Summarization (N18-1)
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| Challenge: | Empirical results show that multiple communicating agents produce a better summary than extractive summarization. |
| Approach: | They propose an encoder-decoder architecture that uses deep communicating agents to represent a long document for abstractive summarization. |
| Outcome: | Empirical results show that multiple communicating agents produce a better summary than baselines. |
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
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| Challenge: | Existing keyphrase extraction methods suffer from data sparsity problem when conducted on short and informal texts. |
| Approach: | They propose a neural keyphrase extraction framework for microblog posts that takes conversation context into account and uses four types of neural encoders to represent conversation context. |
| Outcome: | The proposed framework outperforms state-of-the-art keyphrase extraction methods on Twitter and Weibo datasets. |
Estimating Summary Quality with Pairwise Preferences (N18-1)
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| Challenge: | Existing evaluation systems rely on gold standard summaries but they are expensive and require the availability of experts to achieve high quality. |
| Approach: | They propose an alternative evaluation approach based on pairwise preferences of sentences to provide useful feedback in the form of pairwise preference. |
| Outcome: | The proposed evaluation framework performs better than the three most popular versions of ROUGE with less expensive human input. |
Generating Topic-Oriented Summaries Using Neural Attention (N18-1)
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| Challenge: | Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. |
| Approach: | They propose an attention based RNN framework to generate multiple summaries tuned to different topics of interest. |
| Outcome: | The proposed framework outperforms baselines and shows that attention bias can be successfully used to generate topic-tuned summaries. |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
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| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
Higher-Order Syntactic Attention Network for Longer Sentence Compression (N18-1)
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| Challenge: | Existing sentence compression methods do not handle syntactic features, causing performance degradation . et al. (2015) reported that the longer the input sentences are, the worse the performance becomes. |
| Approach: | They propose a higher-order syntactic attention network that handles higher-level dependency features as an attention distribution on LSTM hidden states. |
| Outcome: | The proposed method outperforms baseline methods on a Google sentence compression dataset. |
Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
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| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function (N18-1)
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| Challenge: | Submodular maximization with the greedy algorithm is an effective approach to extractive summarization. |
| Approach: | They propose a submodular maximization method that is 100 to 400 times faster than existing methods for extractive summarization. |
| Outcome: | The proposed method is 100 to 400 times faster than existing method based on integer-linear-programming formulations and achieves 95%-approximation. |
Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)
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| Challenge: | Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem. |
| Approach: | They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. |
| Outcome: | The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans. |
Relational Summarization for Corpus Analysis (N18-1)
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| Challenge: | Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied. |
| Approach: | They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
| Outcome: | The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis (N18-1)
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| Challenge: | Using movie overviews, we can gain a general impression of a movie by summarizing its content, genre, and artistic style. |
| Approach: | They propose a novel end-to-end model that generates movie overviews from an online database and a multi-label encoder for identifying screenplay attributes. |
| Outcome: | The proposed model reliably assigns good labels for movie attributes and generates sentences conditioned on the identified attributes. |
Which Scores to Predict in Sentence Regression for Text Summarization? (N18-1)
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| Challenge: | Sentence regression is an emerging branch in automatic text summarizations. |
| Approach: | They propose to estimate the importance of information via learned utility scores for individual sentences. |
| Outcome: | The proposed models learn to predict ROUGE recall scores of individual sentences . the models show that following intuition leads to suboptimal results . |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
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| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
Detecting Egregious Conversations between Customers and Virtual Agents (N18-1)
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Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski
| Challenge: | 80% of businesses plan to use chatbots by 2020, according to recent studies . but some bad conversations can be difficult to detect and could lead to litigation . |
| Approach: | They propose a method to detect bad conversations using behavioral cues from the user and patterns in agent responses. |
| Outcome: | The proposed method improves the detection F1 score by 20% over textual features. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
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| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
Variational Knowledge Graph Reasoning (N18-1)
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| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |
Inducing Temporal Relations from Time Anchor Annotation (N18-1)
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| Challenge: | Existing methods for judging temporal relations are limited to “salient” event pairs or on pairs in a fixed window of sentences. |
| Approach: | They propose a new method to obtain temporal relations from absolute time value (a.k.a. time anchors) they start from time anchor for events and time expressions and induced temporal relation annotations automatically . |
| Outcome: | The proposed method shows that it requires less annotation effort and induces inter-sentence relations easily. |
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs (N18-1)
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| Challenge: | Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in Knowledge Graph (KG). |
| Approach: | They propose to densify the Knowledge Graph (KG) with co-occurrence statistics and then use the densified KG to train entity embeddings. |
| Outcome: | The proposed system outperforms state-of-the-art EL systems on benchmark datasets and outperformed state- of-the art systems on sparsely connected entities in the KG. |
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (N18-1)
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| Challenge: | Existing work on court view generation from fact descriptions has improved the working efficiency of legal assistant systems. |
| Approach: | They propose to decode court views conditioned on encoded charge labels from the fact description in a criminal case to improve interpretability of charge prediction systems. |
| Outcome: | The proposed model can generate court views conditioned on encoded charge labels. |
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer (N18-1)
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| Challenge: | Previous work using adversarial methods has struggled to produce high-quality outputs. |
| Approach: | They propose a method that transforms a sentence to alter a specific attribute while preserving its attribute-independent content. |
| Outcome: | The proposed method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets. |
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks (N18-1)
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| Challenge: | Existing approaches to learn to do syntactically controlled paraphrase generation are limited . lexical, pragmatic, and syntaktic variation can hurt generalization of models trained on them . |
| Approach: | They propose a new approach for learning to do syntactically controlled paraphrase generation using a parser. |
| Outcome: | The proposed model generates paraphrases that follow their target specifications without decreasing paraphrase quality compared to baseline models . it improves the robustness of the models to syntactic variation when used to augment training data. |
Sentiment Analysis: It’s Complicated! (N18-1)
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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths
| Challenge: | a dataset of over 7,000 tweets annotated with 5x coverage is used for sentiment analysis . a "complicated" class of sentiment is used to categorize text based on a predefined notion of sentiment . |
| Approach: | They propose to use a "complicated" class of sentiment to categorize tweets . they build a publicly available tweet sentiment analysis dataset . |
| Outcome: | The proposed classifiers perform better over a new publicly available TSA dataset . the classifier performance is compared with existing methods and improves on existing ones . |
Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (N18-1)
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| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
| Approach: | They propose to induce a joint embedding space between disparate label spaces and learning transfer functions between label embeddments to leverage unlabelled data and auxiliary, annotated datasets. |
| Outcome: | The proposed approach outperforms strong single and multi-task baselines and achieves state of the art on aspect-based and topic-based sentiment analysis. |
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (N18-1)
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| Challenge: | a recent shift towards expressive emotion representation models has hampered deep learning in sentiment analysis. |
| Approach: | They propose a multi-task learning problem to solve a language data bottleneck . they propose to use word emotion induction as an individual task to predict emotion . |
| Outcome: | The proposed model outperforms a wide range of other methods on 9 languages and 15 conditions. |
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data (N18-1)
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| Challenge: | Recent research has focused on identifying affective events in text, which are activities or states that positively or negatively affect the people who experience them. |
| Approach: | They propose to categorize affective events based upon human need categories that often explain people’s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. |
| Outcome: | The proposed model learns from unlabeled data and produces significantly better results than individual classifiers. |
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants (N18-1)
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| Challenge: | Existing methods for analyzing warrants in natural language arguments are insufficient. |
| Approach: | They propose a method for reconstructing warrants from news comments . they use a crowdsourcing process to obtain warrants for 2k authentic arguments . |
| Outcome: | The proposed method will define a substantial step towards automatic warrant reconstruction. |
Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (N18-1)
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| Challenge: | a recent study examined deception detection in several domains, including fake reviews, mock crime scenes, and opinions about topics such as abortion or the death penalty. |
| Approach: | They analyze linguistic features in truthful and deceptive interview dialogues . they also examine interviewer perceptions of deception, identifying characteristics of deceptives . |
| Outcome: | The proposed model outperforms human classifications using linguistic features and individual traits. |
Unified Pragmatic Models for Generating and Following Instructions (N18-1)
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| Challenge: | a new technique for layering explicit pragmatic inference on top of models for sequential tasks is proposed . explicit pragmatic reasoning is used to generate and follow natural language instructions . |
| Approach: | They propose a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions and a listener that reasons counterfactually about alternative descriptions. |
| Outcome: | The proposed model improves state-of-the-art models for interpreting human instructions and speaker models in diverse settings. |
Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis (N18-1)
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| Challenge: | Election manifestos document the intentions, motives, and views of political parties. |
| Approach: | They propose a hierarchical structured deep model to predict fine- and coarse-grained positions and a probabilistic soft logic model to perform post-hoc calibration of coarse- and fine-grain positions. |
| Outcome: | The proposed model outperforms state-of-the-art approaches at both granularities using manifestos from twelve countries, written in ten different languages. |
Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness (N18-1)
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| Challenge: | Currently, state-of-the-art models achieve impressive test set performance in the form of accuracy scores. |
| Approach: | They examine the models' robustness to semantically-valid alterations to the input data by identifying three factors and comparing their impact on three SNLI models. |
| Outcome: | The proposed models show that they can generalise to new in-domain instances while also showing that they suffer from insensitivity to small but semantically significant alterations. |
Assessing Language Proficiency from Eye Movements in Reading (N18-1)
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| Challenge: | a novel approach to determine second language proficiency uses behavioral traces of eye movements during reading . over 1.5 billion people are learning English as a second language worldwide . traditional approaches to language proficiency testing have several drawbacks, including the fact that they are typically prepared manually and require extensive resources for test development . |
| Approach: | They propose a method which uses behavioral traces of eye movements during reading to determine learners’ second language proficiency. |
| Outcome: | The proposed approach correlates with standardized English proficiency tests and is validated by eyetracking with eye movements from other readers. |
Comparing Theories of Speaker Choice Using a Model of Classifier Production in Mandarin Chinese (N18-1)
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| Challenge: | Existing studies show that optional reductions are sensitive to contextual predictability . unclear whether speaker choices are driven by audience design or to facilitate production . |
| Approach: | They argue that Uniform Information Density and availability-based production make opposite predictions regarding the predictability of upcoming material and speaker choices. |
| Outcome: | The proposed model shows that speaker choices support availability-based production account, not the UID hypothesis. |
Spotting Spurious Data with Neural Networks (N18-1)
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| Challenge: | Existing methods to identify spurious instances require either annotations generated by each individual annotator or both task-specific and instance-type annotations. |
| Approach: | They propose an approach that discriminates instances based on their "difficulty to learn" they use queueing theory and psychology of learning to improve annotations . |
| Outcome: | The proposed methods outperform state-of-the-art baselines and have a MAP of 0.85 and 0.22 in identifying spurious instances in synthetic and carefully-crowdsourced real-world datasets respectively. |
The Timing of Lexical Memory Retrievals in Language Production (N18-1)
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| Challenge: | In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. |
| Approach: | They propose a cognitive model of fluency in which lexical memory retrievals may explain some of the variability in speech rates. |
| Outcome: | The proposed model predicts that language production is sped up or slowed down depending on activation of a word . |
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing (N18-1)
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| Challenge: | a few researchers have shown that data traces from human processing can be used to improve NLP models. |
| Approach: | They propose to use data readily available for most languages to improve unsupervised induction . they find that english unsupervised POS induction achieves an error reduction of 1.5% . |
| Outcome: | The proposed model improves on Ontonotes domains with a word embeddings. |
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog (N18-1)
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| Challenge: | Existing approaches to reading comprehension on multiparty dialogs have focused on children's stories or newswire. |
| Approach: | They propose a new corpus and a robust deep learning architecture for a task in reading comprehension on multiparty dialog. |
| Outcome: | The proposed model outperforms the state-of-the-art model on a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs. |
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
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| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
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| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics (N18-1)
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| Challenge: | Existing evaluation metrics for NRG models can't measure semantic relevance and diversity of generated results. |
| Approach: | They propose a large-scale domain-specific conversational corpus with preprocessing and cleansing procedures for model training and a testing set for measuring the diversity of generated results. |
| Outcome: | The proposed corpus can be taken as a new benchmark dataset for the NRG task. |
EMR Coding with Semi-Parametric Multi-Head Matching Networks (N18-1)
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| Challenge: | Electronic medical record (EMR) coding is the process of extracting diagnosis and procedure codes from the digital record (the EMR) pertaining to a patient's visit. |
| Approach: | They propose a neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. |
| Outcome: | The proposed model outperforms existing models on a well known de-identified EMR dataset with multi-label performance measures. |
Factors Influencing the Surprising Instability of Word Embeddings (N18-1)
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| Challenge: | Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. |
| Approach: | They examine the stability of word embeddings by examining their properties and analyzing their effects on downstream tasks. |
| Outcome: | The results show that even high frequency words exhibit substantial instability, which can have implications for downstream tasks. |
Mining Evidences for Concept Stock Recommendation (N18-1)
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| Challenge: | a recent announcement of a state plan to build a new economic region has led to the rise of hundreds of stocks . concepts can be useful for investors to find out relevant concept stocks for making investment decisions . a chinese research team uses deep learning to mine evidences from large textual data . |
| Approach: | They use distributed word similarities and deep reinforcement learning to learn a strategy of topic expansion from large scale textual data. |
| Outcome: | The proposed method outperforms a baseline method on two Chinese stock market datasets. |
Binarized LSTM Language Model (N18-1)
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| Challenge: | Long short-term memory (LSTM) language models are widely used for automatic speech recognition and natural language processing (NLP) however, they are limited by the word embedding layer. |
| Approach: | They propose to encode words into binary vectors and use binarized LSTM parameters to achieve high memory compression. |
| Outcome: | The proposed model achieves 11.3 compression ratio without loss of performance and 31.6 compression ratio with acceptable performance degradation. |
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (N18-1)
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Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
| Challenge: | Existing methods for recognizing emotions in conversations ignore inter-speaker dependency relations . dyadic conversations are a form of dialogue between two entities . |
| Approach: | They propose a deep neural framework which leverages contextual information from the conversation history to model past utterances of each speaker into memories. |
| Outcome: | The proposed framework improves by 3 4% over the state-of-the-art in recognizing emotions in dyadic conversational videos. |
How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues (N18-1)
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| Challenge: | Spoken language understanding (SLU) is an essential component in conversational systems. |
| Approach: | They propose a universal time-decay attention mechanism that can be used to decay utterances on the sentence-level and speaker-level. |
| Outcome: | The proposed model significantly improves the state-of-the-art model for contextual understanding performance on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset. |
Towards Understanding Text Factors in Oral Reading (N18-1)
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| Challenge: | Using a case study, we show that variation in oral reading rate is consistent across readers. |
| Approach: | They propose to use text complexity to predict reading rate for professional narrators . they also show that variation can be explained by timing and story-based factors . |
| Outcome: | The authors show that variation in reading rate can be explained by features of the texts being read. |
Generating Bilingual Pragmatic Color References (N18-1)
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| Challenge: | Contextual influences on language often exhibit substantial cross-lingual regularities, but are obscured by semantic and syntactic differences. |
| Approach: | They propose a model that captures language-specific syntax and semantics while also exhibiting responsiveness to contextual difficulty in Chinese and English. |
| Outcome: | The proposed model can identify synonyms between the two languages, even with no exposure to parallel data. |
Learning with Latent Language (N18-1)
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| Challenge: | Using the space of natural language strings as a parameter space is an effective way to capture natural task structure. |
| Approach: | They propose to use natural language as a parameter space for few-shot learning problems including classification, transduction and policy search. |
| Outcome: | The proposed model outperforms models with a linguistic parameterization on image classification, text editing, and reinforcement learning. |
Object Counts! Bringing Explicit Detections Back into Image Captioning (N18-1)
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| Challenge: | Existing approaches to image captioning use explicit object detectors as an intermediate step, but they bypass the explicit detection phase and instead generate captions directly from image embeddings. |
| Approach: | They argue that explicit detections provide rich semantic information and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. |
| Outcome: | The proposed methods can be used to understand why end-to-end captioning systems work well. |
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (N18-1)
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| Challenge: | Existing work suggests that concepts with concrete visual manifestations are easier to learn than abstract ones. |
| Approach: | They propose an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. |
| Outcome: | The proposed algorithm predicts the capacity of machine learning algorithms to learn textual/visual relationships. |
Speaker Naming in Movies (N18-1)
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| Challenge: | Identifying speakers and their names in movies is a primary task for many video analysis problems, such as automatic subtitle labeling. |
| Approach: | They propose a model that leverages visual, textual, and acoustic modalities in an unified optimization framework for speaker naming in movies. |
| Outcome: | The proposed model outperforms baseline models on the MovieQA 2017 challenge for speaker naming in movies and TV shows on visual, textual, and acoustic modalities. |
Stacking with Auxiliary Features for Visual Question Answering (N18-1)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that requires systems to reason about natural language and vision. |
| Approach: | They propose four categories of auxiliary features for ensembling for VQA . three out of the four categories can be inferred from an image-question pair . fourth category uses model-specific explanations . |
| Outcome: | The proposed techniques improve performance for visual question answering (VQA) given an image and a natural language question, the task is to provide an accurate natural language answer. |
Deep Contextualized Word Representations (N18-1)
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
| Challenge: | a new type of deep contextualized word representation is proposed for language understanding problems . word vectors are learned functions of the internal states of a deep bidirectional language model . |
| Approach: | They propose a new type of deep contextualized word representation that models complex features of word use and how they vary across linguistic contexts. |
| Outcome: | The proposed representations improve the state of the art across six challenging NLP problems. |
Learning to Map Context-Dependent Sentences to Executable Formal Queries (N18-1)
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| Challenge: | Existing models that map utterances to executable queries are context-dependent and can incorporate interaction history. |
| Approach: | They propose a context-dependent model that maps utterances to executable queries . their approach combines implicit and explicit modeling of references between utterations . |
| Outcome: | The proposed model can map utterances to executable queries based on interaction history . key to mapping utterrances to queries is resolving references . |
Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
| Outcome: | The proposed model improves mention generation, sentence selection, and sentence generation. |
Recurrent Neural Networks as Weighted Language Recognizers (N18-1)
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| Challenge: | Recent experiments show that RNNs outperform other methods in assigning high probability to held-out English text. |
| Approach: | They focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax . they show that most problems for such RNN are undecidable . |
| Outcome: | The proposed model outperforms other methods in assigning high probability to held-out English text. |