Proceedings of the 2019 Conference of the North
Entity Recognition at First Sight: Improving NER with Eye Movement Information (N19-1)
Copied to clipboard
| Challenge: | Previous studies have shown eye-tracking data can be used to improve natural language processing models. |
| Approach: | They leverage eye movement features from three corpora with recorded gaze information to augment a neural model for named entity recognition with gaze embeddings. |
| Outcome: | The proposed model outperforms baseline models on both individual datasets and in cross-domain settings. |
The emergence of number and syntax units in LSTM language models (N19-1)
Copied to clipboard
| Challenge: | a recent study shows that LSTMs can capture syntax-sensitive generalizations such as long-distance number agreement. |
| Approach: | They investigate the inner mechanics of number tracking in LSTMs at the single neuron level . they find that long-distance number information is largely managed by two "number units" importantly, the behaviour of these units is partially controlled by other units to track syntactic structure . |
| Outcome: | The proposed model is based on a language model with a long-distance number agreement task. |
Neural Self-Training through Spaced Repetition (N19-1)
Copied to clipboard
| Challenge: | Existing methods for self-training rely on predetermined policies to sample unlabeled data. |
| Approach: | They propose a semi-supervised learning approach that uses spaced repetition to dynamically sample informative and diverse unlabeled instances with respect to individual learner and instance characteristics. |
| Outcome: | The proposed model outperforms existing semi-supervised learning approaches on publicly-available datasets. |
Neural language models as psycholinguistic subjects: Representations of syntactic state (N19-1)
Copied to clipboard
| Challenge: | a recent study examines the extent to which neural network language models reflect incremental representations of syntactic state . we examine neural network model behavior on sentences chosen to probe specific aspects of the learned representations . |
| Approach: | They employ experimental methodologies developed in psycholinguistics to study syntactic representation in the human mind. |
| Outcome: | The proposed models are trained on large datasets and only sensitive to subtle cues . the results raise questions about the accuracy of the models and their performance . |
Understanding language-elicited EEG data by predicting it from a fine-tuned language model (N19-1)
Copied to clipboard
| Challenge: | Existing studies have only found two of the ERPs to be predictable from embeddings of a stream of language. |
| Approach: | They propose to fine tune a language model to predict ERPs by embedding a stream of language into a model that allows them to be more accurate. |
| Outcome: | The proposed model fine tunes the ERPs to predict them for the first time. |
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation (N19-1)
Copied to clipboard
| Challenge: | Pre-training on high-resource automatic speech recognition (ASR) tasks improves ST performance even when source language is low-resourced. |
| Approach: | They propose a method to improve direct speech-to-text translation when source language is low-resource . they pre-train model on high-res automatic speech recognition task and fine-tune parameters for ST . |
| Outcome: | The proposed approach improves Spanish English ST even when the source language is low-resource . the pre-trained encoder accounts for most of the improvement, the authors show . |
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (N19-1)
Copied to clipboard
| Challenge: | Xitsonga and English are typologically unrelated languages . phonological features are not directly observed by humans . |
| Approach: | They deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages. |
| Outcome: | The proposed model is well represented in both languages, while others are less so. |
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection (N19-1)
Copied to clipboard
| Challenge: | Disfluencies in spontaneous speech are associated with prosodic disruptions. |
| Approach: | They propose a method to extract acoustic-prosodic cues from word transcripts . they explore early and late fusion techniques for integrating text and prosody . |
| Outcome: | The proposed approach shows gains over a high-accuracy text-only model. |
Massively Multilingual Adversarial Speech Recognition (N19-1)
Copied to clipboard
| Challenge: | Prior work in multilingual and cross-lingual speech recognition has been limited to a subset of the world's most-spoken languages. |
| Approach: | They propose to use phonemes and phonemes as pretraining objectives to encourage language-independent representations. |
| Outcome: | The proposed model is able to learn language-independent representations of speech using multilingual training. |
Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation (N19-1)
Copied to clipboard
| Challenge: | Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus indicate the promise of our proposed method. |
| Approach: | They propose a task of predicting which terminology simultaneous interpreters will leave untranslated using supervised sequence taggers. |
| Outcome: | The proposed method predicts which terminology interpreters leave untranslated . it is based on an annotated version of the NAIST Simultaneous Translation Corpus . |
AudioCaps: Generating Captions for Audios in The Wild (N19-1)
Copied to clipboard
| Challenge: | a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years . |
| Approach: | They propose a top-down multi-scale encoder and aligned semantic attention for audio captioning. |
| Outcome: | The proposed captions are faithful to audio inputs and better than existing models. |
“President Vows to Cut <Taxes> Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines (N19-1)
Copied to clipboard
| Challenge: | Existing datasets address specific humor templates, such as funny one-liners and filling in Mad Libs R. |
| Approach: | They introduce a dataset for research in computational humor that uses crowdsourced editing techniques to create funny headlines. |
| Outcome: | The new dataset supports classic theories of humor, including incongruity, superiority, setup/punchline. |
Answer-based Adversarial Training for Generating Clarification Questions (N19-1)
Copied to clipboard
| Challenge: | a goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. |
| Approach: | They propose a model where hypothetical answers are latent variables that can guide the model into generating more useful clarification questions. |
| Outcome: | The proposed model outperforms retrieval-based models and ablations that exclude utility model and adversarial training on two datasets. |
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
Topic-Guided Variational Auto-Encoder for Text Generation (N19-1)
Copied to clipboard
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
| Challenge: | Experimental results show that our model outperforms its competitors on both unconditional and conditional text generation. |
| Approach: | They propose a topic-guided variational auto-encoder model for text generation that specifies a Gaussian mixture model and a neural topic module to generate sentences under the topic. |
| Outcome: | The proposed model outperforms existing variational auto-encoders on unconditional and conditional text generation, and can generate semantically-meaningful sentences with various topics. |
Implementation of a Chomsky-Schützenberger n-best parser for weighted multiple context-free grammars (N19-1)
Copied to clipboard
| Challenge: | Constituent parsing has been studied extensively in the last decades. |
| Approach: | They propose to decompose a language into a regular language, a homomorphism, and a bracket language to divide the parsing problem into simpler subproblems. |
| Outcome: | The proposed approach is comparable to state-of-the-art grammar-based parsers. |
Phylogenic Multi-Lingual Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | phylogenetic learning is beneficial for low resourced languages and well furnished languages families. |
| Approach: | They propose to use the phylogenetic tree to guide the learning of multi-lingual dependency parsers . they use a phylogy tree to train models that leverage languages structural similarities . |
| Outcome: | The proposed model outperforms independently learned models on zero-shot parsing of unseen languages. |
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (N19-1)
Copied to clipboard
| Challenge: | Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse. |
| Approach: | They propose a transition system that uses a set of parsing items with constant-time random access instead of storing subtrees in a stack . |
| Outcome: | The proposed system constructs a discontinuous constituency tree in 4n–2 transitions for a sentence of length n. |
How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. (N19-1)
Copied to clipboard
| Challenge: | Using annotation variation principles, Part-of-Speech tagging performance degrades when applied to test sentences that depart from training data. |
| Approach: | They propose to use the annotation variation principle to identify inconsistencies between annotations . they also evaluate their impact on prediction performance . |
| Outcome: | The proposed method can detect errors in gold standard annotations and improve prediction performance. |
CCG Parsing Algorithm with Incremental Tree Rotation (N19-1)
Copied to clipboard
| Challenge: | Combinatory Categorial Grammar (CCG) is a mildly context sensitive grammar formalism that excels in incremental sentence processing. |
| Approach: | They propose a new incremental parsing algorithm that uses a syntactic approach . it uses right-branching constituent structures and optional constituents that adjoin on the right . |
| Outcome: | The proposed algorithm can cover the whole CCGbank with greater incrementality and accuracy than previous proposals. |
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing (N19-1)
Copied to clipboard
| Challenge: | Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing tasks. |
| Approach: | They propose a cyclical annealing schedule which repeats the process of increasing multiple times to learn more meaningful latent codes progressively by leveraging previous learning cycles as warm re-restart. |
| Outcome: | The proposed method improves on a broad range of NLP tasks, including language modeling, dialog response generation and semi-supervised text classification. |
Recurrent models and lower bounds for projective syntactic decoding (N19-1)
Copied to clipboard
| Challenge: | a string of recent work has attempted to delve into the formal properties of neural network topology choices. |
| Approach: | They propose to use recurrent models to perform projective maximum spanning tree decoding . they also prove the lower bounds of projective maximal spanning trees . |
| Outcome: | The proposed model can perform better than Eisner's model, proving it impossible to predict a projective MST. |
Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings (N19-1)
Copied to clipboard
| Challenge: | ellipsis is a natural language phenomenon where part of a sentence is missing and its information must be recovered from its context. |
| Approach: | They develop models for embedding VP-elliptical sentences using word embeddments . they extend existing verb disambiguation and sentence similarity datasets to elliptic phrases . |
| Outcome: | The proposed models outperform existing models on verb disambiguation and sentence similarity datasets and their linear counterparts. |
Neural Finite-State Transducers: Beyond Rational Relations (N19-1)
Copied to clipboard
| Challenge: | a finite state transducer defines joint and conditional probability distributions over strings . a weighted finite-state transducers can only model certain functions, known as the rational relations . |
| Approach: | They propose a family of string transduction models defining joint and conditional probability distributions over pairs of strings. |
| Outcome: | The proposed models are more powerful than previous finite-state models with neural features. |
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling (N19-1)
Copied to clipboard
| Challenge: | Empirical experiments show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse. |
| Approach: | They propose a Variational Wasserstein Autoencoder with Riemannian Normalizing Flow to solve this problem by transforming a latent variable into a space that respects the geometric characteristics of input space. |
| Outcome: | Empirical results show that the proposed model avoids KLvanishing and has better performance in language modeling, likelihood approximation, and text generation tasks. |
A Study of Incorrect Paraphrases in Crowdsourced User Utterances (N19-1)
Copied to clipboard
| Challenge: | Developing bots requires high quality training samples, especially for unqualified crowd workers. |
| Approach: | They propose an annotated dataset for detecting quality issues in crowdsourced paraphrasing . they propose to use existing tools and services to provide baselines for identifying issues . |
| Outcome: | The proposed dataset provides a baseline for detecting unqualified paraphrases. |
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
Copied to clipboard
| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |
FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase (N19-1)
Copied to clipboard
| Challenge: | Using FreebaseQA, we can generate over 54K matches from about 28K unique questions with minimal cost. |
| Approach: | They propose a data set for open-domain factoid question answering tasks over structured knowledge bases, like Freebase, using a combination of trivia-type question-answer pairs and subject-predicate-object triples. |
| Outcome: | The proposed data set generates 54K matches from 28K unique questions with minimal cost. |
Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)
Copied to clipboard
| Challenge: | Knowledge graph based simple question answering is a major area of research in question answering. |
| Approach: | They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches. |
| Outcome: | The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset. |
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to open-domain question answering struggle to retrieve indirectly related evidence when no direct evidence is provided. |
| Approach: | They propose a retriever-reader model that learns to attend on essential terms during the question answering process. |
| Outcome: | The proposed model achieves the state-of-the-art on multiple open-domain QA datasets and achieves a 'reader-reader' level. |
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing work restricts search from one entity to another to the maximum number of hops . a knowledge graph is a powerful graph structure that encodes knowledge to save and organize it . |
| Approach: | They propose an unrestricted-hop framework which relaxes the restriction by using a transition-based search framework. |
| Outcome: | The proposed framework performs well with state-of-the-art models and is competitive without exhaustive searches. |
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing datasets for question answering and machine comprehension (MC) are limited to a single paragraph, or even part of it. |
| Approach: | They propose a bi-directional Attention Entity Graph Convolutional Network (BAG) that leverages relationships between nodes in an entity graph and attention information between a query and the entity graph to generate a prediction. |
| Outcome: | Experimental results show that the proposed network achieves state-of-the-art accuracy on the QAngaroo WIKIHOP dataset. |
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation (N19-1)
Copied to clipboard
| Challenge: | a novel word embedding representation for text documents is proposed . the method is based on the Vector of Locally-Aggregated Descriptors used for image representation . |
| Approach: | They propose a novel representation for text documents based on aggregating word embedding vectors into document embeddables. |
| Outcome: | The proposed representation improves on the Movie Review data set and is 10% better than the state-of-the-art representation. |
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)
Copied to clipboard
Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing frameworks for sentiment and emotion analysis are not efficient for inter-task learning. |
| Approach: | They propose a multi-task learning framework that performs sentiment and emotion analysis together. |
| Outcome: | The proposed framework improves on a CMU-MOSEI dataset for sentiment and emotion analysis. |
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)
Copied to clipboard
| Challenge: | Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect. |
| Approach: | They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference. |
| Outcome: | The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets. |
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)
Copied to clipboard
| Challenge: | Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain. |
| Approach: | They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound . |
| Outcome: | The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect. |
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition (N19-1)
Copied to clipboard
| Challenge: | Using textual features, our proposed HiGRU models achieve at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset. |
| Approach: | They propose a hierarchical gated recurrent unit framework to model word-level inputs and an upper-level GRU to capture contexts of utterance-level embeddings. |
| Outcome: | The proposed framework achieves 8.7%, 7.5%, 6.0% improvement over state-of-the-art methods on three datasets. |
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach (N19-1)
Copied to clipboard
| Challenge: | Negation scope detection is a supervised learning task which relies on negation labels at word level. |
| Approach: | They propose a method that replaces world-level negation labels with document-level sentiment annotations. |
| Outcome: | The proposed approach eliminates the need for world-level negation labels and replaces it with document-level sentiment annotations. |
Simplified Neural Unsupervised Domain Adaptation (N19-1)
Copied to clipboard
| Challenge: | Existing unsupervised domain adaptation methods use neural networks to learn representations that are trained to predict the values of subset of important features called “pivot features.” |
| Approach: | They propose to combine the representation learner and task learner to improve on existing neural domain adaptation algorithms by removing heuristically-selected "pivot features" they show competitive performance with a simpler model. |
| Outcome: | The proposed model outperforms existing models by removing heuristically-selected pivot features. |
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (N19-1)
Copied to clipboard
| Challenge: | Unsupervised BWE methods are evaluated on word translation or word similarity tasks. |
| Approach: | They propose a method that learns sentiment-specific word representations for two languages in a common space without cross-lingual supervision. |
| Outcome: | The proposed method outperforms previous unsupervised BWE methods and even supervised Bwe methods on three language pairs for cross-lingual sentiment analysis. |
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
Copied to clipboard
| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |
Lost in Machine Translation: A Method to Reduce Meaning Loss (N19-1)
Copied to clipboard
| Challenge: | state-of-the-art translation systems often fail in preserving meaning . ambiguity between source and target languages can cause translation problems . |
| Approach: | They propose to use a pre-trained neural sequence-to-sequence model to define a less ambiguous translation system. |
| Outcome: | The proposed system preserves meaning in two languages without compromising translation quality. |
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Existing work has addressed this problem by leveraging monolingual or multilingual data. |
| Approach: | They propose to introduce a differentiable reconstruction loss for neural machine translation to exploit the limited amounts of parallel text available in low-resource settings. |
| Outcome: | The proposed approach achieves small but consistent BLEU improvements on four language pairs in both translation directions and outperforms an alternative differentiable reconstruction strategy based on hidden states. |
Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)
Copied to clipboard
| Challenge: | Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding. |
| Approach: | They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data . |
| Outcome: | The proposed method improves translation quality without hurting unconstrained words. |
Aligning Vector-spaces with Noisy Supervised Lexicon (N19-1)
Copied to clipboard
| Challenge: | Current approaches to learning to translate between two vector spaces assume that the lexicon defines alignment pairs is noise-free. |
| Approach: | They propose a model that accounts for noisy pairs and propose supervised learning problems for this problem. |
| Outcome: | The proposed model significantly improves translation accuracy on bilingual word embedding translation and mapping between diachronic embeddable spaces. |
Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding. |
| Approach: | They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. |
| Outcome: | The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets. |
Content Differences in Syntactic and Semantic Representation (N19-1)
Copied to clipboard
| Challenge: | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. |
| Approach: | They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes. |
| Outcome: | The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. |
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts (N19-1)
Copied to clipboard
| Challenge: | Mimicking has been proposed as a solution to learning high-quality embeddings for rare words because of sparse context information. |
| Approach: | They propose a method to reproduce embeddings of frequent words from their surface form and then use it to compute embedds for rare words. |
| Outcome: | The proposed model outperforms previous work on rare and medium-frequency words. |
Evaluating Style Transfer for Text (N19-1)
Copied to clipboard
| Challenge: | Existing studies on style transfer for text are lacking a standard set of evaluation practices. |
| Approach: | They propose a set of metrics for automated evaluation that are more strongly correlated with human judgment and show tradeoffs between aspects of interest. |
| Outcome: | The proposed models exhibit tradeoffs between aspects of interest and human judgment, demonstrating the importance of evaluating them at specific points of their tradeoff plots. |
Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition (N19-1)
Copied to clipboard
| Challenge: | Existing datasets of semantic relatedness only include pairs of unigrams (single words) Existing data suffer from inconsistent annotations and scale region bias due to rating scales. |
| Approach: | They propose to use a large, fine-grained, bigram relatedness dataset to compare the relatedness of 3,345 English term pairs using a comparative annotation technique called Best–Worst Scaling. |
| Outcome: | The proposed datasets are highly reliable and have a split-half reliability of 0.937. |
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems (N19-1)
Copied to clipboard
Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
| Challenge: | Existing methods to detect outliers in text have been neglected in NLP . outlier detection is a problem in dialog systems where text is often no more than a few sentences in length. |
| Approach: | They propose a technique that uses sentence embeddings to detect outliers in short texts using neural sentence embeds and distance-based outlier detection. |
| Outcome: | The proposed technique detects outliers in a corpus of short texts while generating highly diverse corpora that produce more robust intent classification and slot-filling models. |
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives (N19-1)
Copied to clipboard
| Challenge: | To properly infer the intention of the narrator, one needs a certain degree of common sense and social intuition. |
| Approach: | They propose a task that uses common sense to extract pairs of questions that are appropriate candidates for the task. |
| Outcome: | The proposed method exploits commonalities in experiences people share online to extract pairs of semantically plausible advice-seeking questions that are appropriate candidates for the cloze task. |
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims (N19-1)
Copied to clipboard
| Challenge: | a number of fact checking techniques are used to identify and eliminate biases in text data. |
| Approach: | They propose to use search engines to expand and diversify a dataset of claims, perspectives and evidence to address a selection bias. |
| Outcome: | The proposed approach outperforms existing methods in a language understanding task. |
IMHO Fine-Tuning Improves Claim Detection (N19-1)
Copied to clipboard
| Challenge: | Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. |
| Approach: | They propose to fine-tune a language model using a Reddit corpus of opinionated claims and use the internet acronyms IMO/IMHO to identify claims. |
| Outcome: | The proposed approach improves state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points. |
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog (N19-1)
Copied to clipboard
| Challenge: | Neural network models have gained traction for sentence-level intent classification and token-based slot-label identification. |
| Approach: | They propose a neural network model that performs multi-label classification for identifying multiple intents and produces token-based slot-l labels at the token-level. |
| Outcome: | The proposed model provides a small but statistically significant improvement on the ATIS dataset and 55% accuracy improvement on an internal multi-intent dataset. |
CITE: A Corpus of Image-Text Discourse Relations (N19-1)
Copied to clipboard
| Challenge: | a crowd-sourced resource characterizes inferences in image-text contexts in the domain of cooking recipes . a recent study has found that image-image presentations are more effective at integrating text and image . |
| Approach: | They propose a crowd-sourced resource for multimodal discourse characterizing inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. |
| Outcome: | The proposed corpus enables a better understanding of communication and common-sense reasoning . it is particularly important for automating the understanding and generation of text-image presentations . |
Improving Dialogue State Tracking by Discerning the Relevant Context (N19-1)
Copied to clipboard
| Challenge: | Dialog state tracking (DST) is used to estimate user's goals and requests in order to plan next action and respond accordingly. |
| Approach: | They propose a framework that uses the current user utterance and the most recent system utterant to determine the relevance of a system . Specifically, they use the current and most recent user . and system adverbs to determine relevance. |
| Outcome: | The proposed framework improves goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets over the previous state-of-the-art GLAD model. |
CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog (N19-1)
Copied to clipboard
| Challenge: | Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image. |
| Approach: | They construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset and use it to benchmark performance of standard visual dialog models. |
| Outcome: | The proposed model is based on a large diagnostic dataset for studying multi-round reasoning in visual dialog. |
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
Copied to clipboard
| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
| Outcome: | The proposed classifiers obtained state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without complex metaphor-specific features or deep neural architectures employed by other systems. |
Detection of Abusive Language: the Problem of Biased Datasets (N19-1)
Copied to clipboard
| Challenge: | Recent studies have reported high classification performance on datasets with difficult cases of abusive language. |
| Approach: | They examine the impact of data bias on abusive language detection by focusing on specific microposts rather than random sampling. |
| Outcome: | The proposed method is more accurate and more accurate than random sampling. |
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (N19-1)
Copied to clipboard
| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
| Approach: | They propose methods to reduce gender bias in word embeddings by debiasing them using text corpora. |
| Outcome: | The proposed methods show that they can reduce gender bias in word embeddings . the proposed methods are insufficient and should not be trusted, the authors argue . |
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
| Approach: | They propose a method to debias word embeddings in multiclass settings such as gender and religion, extending the work of Bolukbasi et al. (2016). |
| Outcome: | The proposed method maintains the efficacy in standard NLP tasks while maintaining the utility of embeddings. |
On Measuring Social Biases in Sentence Encoders (N19-1)
Copied to clipboard
| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
| Outcome: | The proposed test shows that word2vec and word2Ve exhibit human-like implicit biases based on gender, race, and other social constructs. |
Gender Bias in Contextualized Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
| Approach: | They conduct several intrinsic analyses to quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. |
| Outcome: | The proposed method mitigates gender bias on WinoBias probing corpus and demonstrates that it can be implemented in other systems. |
Combining Sentiment Lexica with a Multi-View Variational Autoencoder (N19-1)
Copied to clipboard
| Challenge: | a new model of sentiment lexica is being developed to combine disparate scales into a common representation. |
| Approach: | They propose a model that unifies disparate scales into a common latent representation . they evaluate a text classification task using nine English-Language sentiment datasets . |
| Outcome: | The proposed model outperforms six individual sentiment lexica and a simple combination thereof. |
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling (N19-1)
Copied to clipboard
| Challenge: | Existing work on opinion role labeling (ORL) is highly correlative with semantic role labeled (SRL) SRL is used to identify opinion holders and holder expressions for a given predicate. |
| Approach: | They propose a method to enhance opinion role labeling by presenting semantic-aware word representations which are learned from SRL. |
| Outcome: | The proposed method outperforms two other methods on a benchmark MPQA corpus and achieves higher F scores. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
Copied to clipboard
| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
Generalizing Unmasking for Short Texts (N19-1)
Copied to clipboard
| Challenge: | Authorship verification is the problem of inferring whether two texts were written by the same author. |
| Approach: | They propose a generalized unmasking approach which allows for authorship verification of short texts with high precision at an adjustable recall tradeoff. |
| Outcome: | The proposed approach achieves accuracies of 75–80% while allowing for easy adjustment to forensic scenarios that require higher levels of confidence. |
Adversarial Training for Satire Detection: Controlling for Confounding Variables (N19-1)
Copied to clipboard
| Challenge: | Existing methods for satire detection focus on satirical news based on article sources . satiric news are written with the aim of mimicking regular news in diction . |
| Approach: | They propose a model for satire detection with an adversarial component to control for the confounding variable of publication source. |
| Outcome: | The proposed model improves generalization performance to unseen publications with an adversarial component. |
Keyphrase Generation: A Text Summarization Struggle (N19-1)
Copied to clipboard
| Challenge: | Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text. |
| Approach: | They propose to consider the keyphrase string as an abstractive summary of the title and the abstract. |
| Outcome: | The proposed method can generate better keyphrases than the existing methods or the unsupervised ones. |
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (N19-1)
Copied to clipboard
| Challenge: | Neural sequence-to-sequence models are currently the dominant approach in natural language processing tasks, but require massive parallel corpora. |
| Approach: | They propose a sequence-to-sequence-tosequnce autoencoder with words as latent variables . they apply the model to unsupervised abstractive sentence compression . |
| Outcome: | The proposed model achieves promising results in unsupervised sentence compression on benchmark datasets. |
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation (N19-1)
Copied to clipboard
Ori Shapira, David Gabay, Yang Gao, Hadar Ronen, Ramakanth Pasunuru, Mohit Bansal, Yael Amsterdamer, Ido Dagan
| Challenge: | Manual evaluation methods are perceived as insufficient due to the high cost of the Pyramid method and the required expertise. |
| Approach: | They propose a crowdsourced method that compares system summaries to references and uses crowdsourced scripts to analyze the results. |
| Outcome: | The proposed method shows higher correlation relative to the original Pyramid method. |
Serial Recall Effects in Neural Language Modeling (N19-1)
Copied to clipboard
| Challenge: | Recent studies have shed light on the information encoded by LSTM networks. |
| Approach: | They propose to use serial recall experiments to model human memory of words in the order they occur in the language. |
| Outcome: | The proposed model can learn function words much better than content words and can capture syntactic structures such as subject-verb agreement. |
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization (N19-1)
Copied to clipboard
| Challenge: | Concept map-based multi-document summarization is a subtask of CM-MDS that requires a pairwise comparison to improve the summary quality. |
| Approach: | They propose to use locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm to group concepts into a single concept map. |
| Outcome: | The proposed methods exhibit linear and log-linear runtime complexity, making them much more scalable. |
Syntax-aware Neural Semantic Role Labeling with Supertags (N19-1)
Copied to clipboard
| Challenge: | a new syntax-aware model for dependency-based semantic role labeling outperforms syntax-based models for English and Spanish. |
| Approach: | They propose a syntax-aware model for dependency-based semantic role labeling that outperforms syntax-based models for English and Spanish. |
| Outcome: | The proposed model outperforms syntax-agnostic models for English and Spanish. |
Left-to-Right Dependency Parsing with Pointer Networks (N19-1)
Copied to clipboard
| Challenge: | a new algorithm that parses sentences from left to right is simpler than the top-down stack-pointer parser . a graph-based dependency parsing model has been ahead of the curve in terms of accuracy in the past two years . |
| Approach: | They propose a transition-based algorithm that parses sentences from left to right by building n attachments, with n being the length of the input sentence. |
| Outcome: | The proposed algorithm outperforms the top-down stack-pointer parser and is twice as fast as the original top-up stack-pointing parsers. |
Viable Dependency Parsing as Sequence Labeling (N19-1)
Copied to clipboard
| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
Pooled Contextualized Embeddings for Named Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Contextual string embeddings are a recent type of word embeddable that are useful for sequence labeling tasks. |
| Approach: | They propose a method that dynamically aggregates contextualized embeddings of each unique string . they then use a pooling operation to distill a ”global” word representation from all contextualized instances . |
| Outcome: | The proposed method improves state-of-the-art for named entity recognition tasks. |
Better Modeling of Incomplete Annotations for Named Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) assume that the training data is fully annotated with named entity information. |
| Approach: | They propose a supervised setup for named entity recognition where annotated data is assumed to be available during training. |
| Outcome: | The proposed approach is able to recognize named entities with incomplete annotations. |
Event Detection without Triggers (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |
Sub-event detection from twitter streams as a sequence labeling problem (N19-1)
Copied to clipboard
| Challenge: | Existing methods for sub-event detection do not account for sequential nature of social media streams. |
| Approach: | They propose to use a neural sequence architecture that explicitly accounts for the chronological order of posts to improve sub-event detection. |
| Outcome: | The proposed method outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F1 improvement) it also outperformed a recurrent neural network model on the posts sequence level for labeled sub- events (2.4% bin-level improvement). |
GraphIE: A Graph-Based Framework for Information Extraction (N19-1)
Copied to clipboard
| Challenge: | Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies. |
| Approach: | They propose a framework that operates over a graph representing a broad set of dependencies between textual units. |
| Outcome: | The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks. |
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
Copied to clipboard
| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
| Outcome: | The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data. |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
Copied to clipboard
| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)
Copied to clipboard
| Challenge: | Argument compatibility is a linguistic condition that is often used in event coreference resolution systems. |
| Approach: | They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions. |
| Outcome: | The proposed model improves the performance of the overall event coreference model on the English dataset. |
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets. |
| Approach: | They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple benchmarks. |
Description-Based Zero-shot Fine-Grained Entity Typing (N19-1)
Copied to clipboard
| Challenge: | Existing systems consider a small set of coarse types, but fine-grained Entity Typing can be used for a variety of tasks. |
| Approach: | They propose a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. |
| Outcome: | The proposed method is able to recognize novel types without additional training on a public benchmark dataset. |
Adversarial Decomposition of Text Representation (N19-1)
Copied to clipboard
| Challenge: | a new method for adversarial decomposition of text representations is proposed . it is capable of fine-grained controlled change of different aspects of the input sentence . |
| Approach: | They propose a method for adversarial decomposition of text representation . they use vectors responsible for a specific aspect of the input sentence . |
| Outcome: | The proposed method outperforms the embeddings of a regular autoencoder on paraphrase detection tasks. |
PoMo: Generating Entity-Specific Post-Modifiers in Context (N19-1)
Copied to clipboard
Jun Seok Kang, Robert Logan, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian
| Challenge: | Using crowdsourcing, we show that contextual relevance is necessary for accurate post-modifier generation. |
| Approach: | They introduce entity post-modifier generation as an instance of a collaborative writing task . they build a post- modifier dataset from news articles that provides contextually relevant information about the target entity. |
| Outcome: | The proposed system can generate a post-modifier phrase that provides contextually relevant information about the target entity. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
Copied to clipboard
J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network (N19-1)
Copied to clipboard
| Challenge: | In order to ensure customer satisfaction and retention, it is imperative for customer care agents and chatbots to be cordial and emphatic to the customer. |
| Approach: | They propose a deep learning framework that automatically transforms neutral customer care responses into courteous replies by stylistic transfer. |
| Outcome: | The proposed model can generate courteous expressions consistent with the emotional state of the customer while preserving the content. |
How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to generate metaphors rely on template-based or rule-based knowledge, which constrains the diversity of generated metaphors. |
| Approach: | They propose a neural approach to metaphor generation that uses wiki corpus to extract metaphorically used verbs and train a language model. |
| Outcome: | The proposed approach generates metaphors with good readability and creativity using wiki corpus and automatic metrics and human evaluations. |
Incorporating Context and External Knowledge for Pronoun Coreference Resolution (N19-1)
Copied to clipboard
| Challenge: | Existing models for pronoun coreference resolution rely on manual definitions and features to resolve pronounous coreferences. |
| Approach: | They propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models by a large margin. |
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)
Copied to clipboard
| Challenge: | Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases. |
| Approach: | They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP). |
| Outcome: | The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches. |
Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)
Copied to clipboard
| Challenge: | Pronouns are often dropped in conversational genres as their referents can be easily understood from context. |
| Approach: | They propose an end-to-end neural network model to recover dropped pronouns in conversational data. |
| Outcome: | The proposed model improves on three different conversational genres. |
The problem with probabilistic DAG automata for semantic graphs (N19-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) annotations are directed acyclic graphs, but most probabilistic models view them as strings or trees. |
| Approach: | They show that some DAG automata cannot be made into useful probabilistic models by assigning weights to transitions. |
| Outcome: | The proposed model can't be made into useful probabilistic models by assigning weights to transitions . the proposed model is not feasible for all variants, but it is problematic for planar variants if they are not rooted . |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
Copied to clipboard
| Challenge: | Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing. |
| Approach: | They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components. |
| Outcome: | The proposed framework allows for easy experimentation with different segmentation and composition components, as well as advanced techniques based on position embeddings and self-attention. |
Better Word Embeddings by Disentangling Contextual n-Gram Information (N19-1)
Copied to clipboard
| Challenge: | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. |
| Approach: | They show that word embeddings with bigram and trigram embedds improve unigram embeds . they claim this removes contextual information from unigrammes, resulting in better unigraph embedders . |
| Outcome: | The proposed model outperforms competing models on a wide variety of tasks. |
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process (N19-1)
Copied to clipboard
| Challenge: | Topic models are used to extract topical structures from document-word frequency representations of the text corpus without supervision. |
| Approach: | They propose a Bayesian nonparametric topic modeling with knowledge graph embedding to employ knowledge graphs to extract more coherent topics. |
| Outcome: | The proposed model performs better on three public datasets than state-of-the-art models on topic coherence and document classification accuracy. |
Correlation Coefficients and Semantic Textual Similarity (N19-1)
Copied to clipboard
| Challenge: | Existing research into semantic textual similarity has focused on word embeddings . little attention has been devoted to similarity measures between word embeds - a new study shows . |
| Approach: | They show that cosine similarity is essentially equivalent to the Pearson correlation coefficient for all common word vectors. |
| Outcome: | The proposed model outperforms the existing model on word-level and sentence-level similarity benchmarks. |
Generating Token-Level Explanations for Natural Language Inference (N19-1)
Copied to clipboard
| Challenge: | Existing methods to generate token-level explanations for NLI on single sentences have not been tested. |
| Approach: | They propose to generate token-level explanations for NLI without explicitly annotating training data. |
| Outcome: | The proposed approach is faster and more accurate than the black-box methods. |
Strong Baselines for Complex Word Identification across Multiple Languages (N19-1)
Copied to clipboard
Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
| Challenge: | Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a specific type of reader. |
| Approach: | They propose to use monolingual and cross-lingual CWI models to make predictions for languages not seen during training. |
| Outcome: | The proposed models perform as well as (or better than) most models submitted to the latest CWI Shared Task. |
Adaptive Convolution for Multi-Relational Learning (N19-1)
Copied to clipboard
| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion (N19-1)
Copied to clipboard
| Challenge: | Knowledge graph embedding models are so called-black box and are hard to interpret. |
| Approach: | They propose to use graph patterns to construct an entity ranking system for each graph pattern and evaluate them using a ranking system. |
| Outcome: | The proposed model outperforms other state-of-the-art models on standard metrics such as HITS@n and MRR. |
Adversarial Training for Weakly Supervised Event Detection (N19-1)
Copied to clipboard
| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets. |
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems (N19-1)
Copied to clipboard
| Challenge: | Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
| Approach: | They propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. |
| Outcome: | The proposed model improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others. |
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (N19-1)
Copied to clipboard
| Challenge: | Distant supervision is an important paradigm for automatically extracting relations . but the examples collected can be noisy and pose significant challenge for labeling . |
| Approach: | They propose a method to predict whether two entities participate in a relation at a given time spot. |
| Outcome: | The proposed model performs better in WIKI-TIME and NYT-10 datasets compared with the best existing models . the proposed model is based on a dataset with a valid period of a certain relation of two entities in the knowledge base . |
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to classify text documents of emerging classes are ineffective because of insufficient or even unavailable training data. |
| Approach: | They propose a two-phase framework with data augmentation and feature augmentation to deal with unseen classes effectively using four kinds of semantic knowledge. |
| Outcome: | The proposed framework achieves the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario. |
Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences (N19-1)
Copied to clipboard
| Challenge: | Existing word embedding algorithms make a strong assumption that words are semantically related only if they co-occur locally within a window of fixed size. |
| Approach: | They propose a graph-based word embedding method that relies on locality to capture the semantic association between words that co-occur frequently but non-locally within documents. |
| Outcome: | The proposed method outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization. |
Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings (N19-1)
Copied to clipboard
| Challenge: | Existing distributional semantic models cannot capture the distinct meanings of polysemous words, resulting in conflated word representations of diverse contextual semantics. |
| Approach: | They propose a distributional semantic model that learns multiple representations of a word based on different topics. |
| Outcome: | The proposed model outperforms single-prototype models on NLP downstream tasks. |
What just happened? Evaluating retrofitted distributional word vectors (N19-1)
Copied to clipboard
| Challenge: | Recent work has attempted to enhance vector space representations using information from structured semantic resources. |
| Approach: | They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting. |
| Outcome: | The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric. |
Linguistic Knowledge and Transferability of Contextual Representations (N19-1)
Copied to clipboard
| Challenge: | Recent work has explored contextual word representations, which assign each word a vector that is a function of the entire input sequence. |
| Approach: | They compare pretrained word representations with 16 diverse probing tasks to examine their transferability. |
| Outcome: | The pretrained representations are successful across a diverse set of NLP tasks . the models are competitive with state-of-the-art models but fail on fine-grained tasks requiring fine-granular knowledge, the study finds . |
Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction (N19-1)
Copied to clipboard
| Challenge: | Using stochastic gradient descent, part-of-speech (POS) induction is a challenging task. |
| Approach: | They propose to maximize mutual information between the induced label and its context by maximizing mutual information. |
| Outcome: | The proposed approach achieves strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
Copied to clipboard
| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
| Outcome: | The proposed model outperforms standard sequential language models and improves parsing performance. |
Cooperative Learning of Disjoint Syntax and Semantics (N19-1)
Copied to clipboard
| Challenge: | Existing models that learn to jointly infer an expression’s syntactic structure and its semantics fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. |
| Approach: | They propose a recursive model that learns to jointly infer an expression’s syntactic structure and its semantics without requiring a formal supervision. |
| Outcome: | The proposed model performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis. |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (N19-1)
Copied to clipboard
| Challenge: | Using the deep inside-outside recursive autoencoder, we can extract both shallow parses and full syntactic trees from any domain or language automatically. |
| Approach: | They propose a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. |
| Outcome: | The proposed method outperforms previous methods on the WSJ dataset. |
Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Current language models are unable to efficiently model entity names observed in text providing insufficient context. |
| Approach: | They propose to augment a traditional model with an external knowledge base to model entity names observed in text. |
| Outcome: | The proposed model improves on a Named Entity Recognition (NER) task by requiring no additional information such as named entity tags. |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
Copied to clipboard
| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
Competence-based Curriculum Learning for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Existing NMT systems require specialized heuristics and large batch sizes. |
| Approach: | They propose a curriculum learning framework for NMT that reduces training time and costs . framework consists of a principled way of deciding which training samples are shown to the model . |
| Outcome: | The proposed framework can reduce training time and improve performance of recurrent neural network models and Transformers. |
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Back-translation has been used in previous approaches for unsupervised neural machine translation, but pseudo sentences are of low quality as translation errors accumulate during training. |
| Approach: | They propose an approach to extract and edit real sentences from monolingual corpora and introduce a comparative translation loss to evaluate the translated target sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art translation systems across two benchmarks and two low-resource language pairs by more than 2 BLEU points. |
Consistency by Agreement in Zero-Shot Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | In this paper, we focus on zero-shot generalization—a challenging setup that tests models on translation directions they have not been optimized for at training time. |
| Approach: | They propose a method that allows for a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. |
| Outcome: | The proposed model improves on public zero-shot translation benchmarks without loss of performance on supervised translation directions. |
Modeling Recurrence for Transformer (N19-1)
Copied to clipboard
| Challenge: | Existing studies show that the lack of recurrence modeling hinders the development of a translation model. |
| Approach: | They propose to model recurrence for Transformer with an additional recurrent encoder. |
| Outcome: | The proposed model outperforms the deep model on EnglishGerman and ChineseEnglish translation tasks. |
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
| Approach: | They propose a latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods to induce its own action space from the data. |
| Outcome: | The proposed framework achieves better performance than word-level policy gradient methods on DealOrNoDeal and MultiWoz dialogs. |
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)
Copied to clipboard
| Challenge: | Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory . |
| Approach: | They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response . |
| Outcome: | The proposed approach significantly improves the informativeness of the generated responses. |
Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
Copied to clipboard
| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |
Disentangling Language and Knowledge in Task-Oriented Dialogs (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to handle task-oriented dialogs break when asked to handle such changes. |
| Approach: | They propose an encoder-decoder architecture with a novel Bag-of-Sequences memory which facilitates the disentangled learning of the response’s language model and its knowledge incorporation. |
| Outcome: | The proposed architecture outperforms state-of-the-art models on bAbI OOV test sets and other human-human datasets and shows that it is robust to KB modifications. |
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)
Copied to clipboard
| Challenge: | Neural networks equipped with self-attention have parallelizable computation and the ability to capture both long-range and local dependencies. |
| Approach: | They propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" it captures pairwise and global dependencies by a compatibility function composed of dot-product and additive attentions . |
| Outcome: | The proposed model outperforms CNN-/RNN-/attention-based models on nine NLP benchmarks with compelling memory- and time-efficiency. |
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (N19-1)
Copied to clipboard
| Challenge: | Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. |
| Approach: | They propose a large-scale Word in Context dataset, called WiC, which is curated by experts and can be used to evaluate context-sensitive representations. |
| Outcome: | The proposed models outperform the standard evaluation dataset for the purpose and highlight their shortcomings. |
Does My Rebuttal Matter? Insights from a Major NLP Conference (N19-1)
Copied to clipboard
| Challenge: | Peer review is a core element of the scientific process, but few studies have evaluated its properties empirically. |
| Approach: | They propose to use peer review to assess the effectiveness of rebuttal phase in NLP conferences. |
| Outcome: | The proposed task predicts after-rebuttal scores from initial reviews and author responses. |
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism (N19-1)
Copied to clipboard
| Challenge: | Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. |
| Approach: | They propose to use a literary theory to analyze Italo Calvino's novel Invisible Cities to leverage contextualized representations to embed each city's description and use unsupervised methods to cluster embeddings. |
| Outcome: | The proposed method can be applied to Italo Calvino’s novel Invisible Cities . authors compare results to similarity judgments generated by human readers . |
PAWS: Paraphrase Adversaries from Word Scrambling (N19-1)
Copied to clipboard
| Challenge: | Existing paraphrase identification datasets lack sentence pairs with high word overlap without being paraphrases. |
| Approach: | They propose a workflow for generating pairs of sentences with high word overlap . they use controlled word swapping and back translation followed by fluency and paraphrase judgments . |
| Outcome: | The proposed dataset has 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. |
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough? (N19-1)
Copied to clipboard
| Challenge: | Existing studies have evaluated grammatical error correction models on a single corpus, but the evaluation is incomplete because the task difficulty varies depending on the corpus and conditions such as proficiency levels of the writers and essay topics. |
| Approach: | They evaluate the performance of several GEC models against various learner corpora and compare their rankings against the corpus. |
| Outcome: | The evaluation of several models against learner corpora shows that the models’ rankings vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models. |
Star-Transformer (N19-1)
Copied to clipboard
| Challenge: | Existing models with fully-connected attention connections are heavy and require large training data. |
| Approach: | They propose a lightweight alternative to the Transformer by sparsifying the fully-connected structure with a star-shaped topology. |
| Outcome: | The proposed model achieves significant performance improvements on 22 datasets on four tasks. |
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation (N19-1)
Copied to clipboard
| Challenge: | asynchronous domains lack large labeled datasets to train an effective speech act recognition model. |
| Approach: | They propose methods to leverage abundant unlabeled conversational data and available labeled data from synchronous domains to train an effective SAR model. |
| Outcome: | The proposed method outperforms existing methods when trained on in-domain data only. |
From legal to technical concept: Towards an automated classification of German political Twitter postings as criminal offenses (N19-1)
Copied to clipboard
| Challenge: | 'Network Enforcement Act' provides for a regulatory framework for 'illegal content' on social network platforms like Twitter or Facebook. |
| Approach: | They propose a data annotation schema to determine whether a particular tweet could constitute a criminal offense and a binary classification schema to help with this. |
| Outcome: | The proposed schema shows that the majority of offensive posts do not constitute a criminal offense and still contribute to public discourse. |
Joint Multi-Label Attention Networks for Social Text Annotation (N19-1)
Copied to clipboard
| Challenge: | Present research shows that title metadata could affect social annotation. |
| Approach: | They propose a title-guided attention network for document annotation with user-generated tags that separates the title from the content of a document and applies a semantic-based loss regulariser over each sentence in the content. |
| Outcome: | The proposed approach outperforms the Bi-GRU and Hierarchical Attention Network (HAN) on two open datasets with 10%-30% reduction in training time. |
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
Copied to clipboard
| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |
Detecting Cybersecurity Events from Noisy Short Text (N19-1)
Copied to clipboard
Semih Yagcioglu, Mehmet Saygin Seyfioglu, Begum Citamak, Batuhan Bardak, Seren Guldamlasioglu, Azmi Yuksel, Emin Islam Tatli
| Challenge: | Using domain-specific word embeddings, we propose a method to detect cyber security events from noisy short text. |
| Approach: | They propose a method that leverages domain-specific word embeddings and task-specific features to detect cyber security events from tweets. |
| Outcome: | The proposed model outperforms both baselines and traditional models on a dataset of 2K tweets and manually annotates them. |
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks (N19-1)
Copied to clipboard
| Challenge: | Recent work in natural language processing generates adversarial examples using white-box access . a neural network can learn to emulate the behavior of a white- box attack and generalize well to new examples. |
| Approach: | They propose an adversarial training approach that assumes white-box access to an attacker's model and optimizes the input directly against it. |
| Outcome: | The proposed approach reduces example generation time by 19x-39x and exposes the Google Perspective API vulnerability. |
Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media (N19-1)
Copied to clipboard
| Challenge: | 6,000 tweets describe software vulnerabilities, which are shared across a range of websites and social media platforms. |
| Approach: | They propose a method to link software vulnerabilities reported in tweets to CVEs in the National Vulnerability Database (NVD) a Precision@50 of 0.86 is achieved when forecasting high severity vulnerabilities, they show . |
| Outcome: | The proposed method outperforms baseline methods based on tweet volume and the language used to describe them online. |
Fake News Detection using Deep Markov Random Fields (N19-1)
Copied to clipboard
| Challenge: | Existing deep-learning-based methods ignore the correlations among news articles and only consider each article individually. |
| Approach: | They propose a graph-theoretic method that inherits the power of deep learning while utilizing the correlations among the articles. |
| Outcome: | The proposed model improves on state-of-the-art models on well-known datasets. |
Issue Framing in Online Discussion Fora (N19-1)
Copied to clipboard
| Challenge: | In online discussion fora, speakers often make arguments by highlighting certain aspects of the topic. |
| Approach: | They propose to use a newswire and social media annotated corpus to detect issue frames in online discussions. |
| Outcome: | The proposed model can be applied to the domain of discussion fora using multi-task and adversarial training. |
Vector of Locally Aggregated Embeddings for Text Representation (N19-1)
Copied to clipboard
| Challenge: | Existing models for text classification use word embeddings, weighted averaging, and deepening networks. |
| Approach: | They propose a vector-based locally averaging model that encodes each input text by effectively identifying and integrating its semantically-relevant parts. |
| Outcome: | The proposed model outperforms RNNs and CNNs in text classification while taking only a fraction of their training time. |
Predicting the Type and Target of Offensive Posts in Social Media (N19-1)
Copied to clipboard
| Challenge: | Prior work focused on detecting specific types of offensive content, such as hate speech, cyberbullying, or cyber-aggression. |
| Approach: | They propose to use a dataset to identify offensive content in social media . they compare the performance of different machine learning models to OLID . |
| Outcome: | The proposed dataset contains tweets annotated for offensive content using a fine-grained three-layer annotation scheme. |
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (N19-1)
Copied to clipboard
| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |
Detecting cognitive impairments by agreeing on interpretations of linguistic features (N19-1)
Copied to clipboard
| Challenge: | Linguistic features have shown promising applications for detecting cognitive impairments. |
| Approach: | They propose a framework to classify after reaching agreements between modalities by using linguistic features to divide linguistic subsets into subset and let neural networks learn low-dimensional representations that agree with each other. |
| Outcome: | The proposed framework outperforms existing classifiers using all of the 413 linguistic features. |
Relation Extraction using Explicit Context Conditioning (N19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem . |
| Approach: | They propose to use second-order relations to compute relation scores for relation extraction (RE) . they propose to combine second- and first-order relation scores to obtain final relation scores . |
| Outcome: | The proposed method leads to state-of-the-art performance over two biomedical datasets. |
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues (N19-1)
Copied to clipboard
| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
| Approach: | They develop a pre-trained conversation model that learns to classify client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. |
| Outcome: | The proposed model outperforms state-of-the-art comparison models and shows expected linguistic patterns for each category. |
Using Similarity Measures to Select Pretraining Data for NER (N19-1)
Copied to clipboard
| Challenge: | Existing studies on how to select appropriate data to pretrain word vectors or LMs are lacking. |
| Approach: | They propose to quantify aspects of similarity between pretraining and target data. |
| Outcome: | The proposed measures are good predictors of the usefulness of pretrained models for Named Entity Recognition over 30 data pairs. |
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction (N19-1)
Copied to clipboard
| Challenge: | Modern NLP systems require high-quality annotations, but experts are expensive and lay annotators may not have the knowledge to provide high- quality annotations. |
| Approach: | They propose to directly model instance difficulty to improve model performance and to route instances to appropriate annotators. |
| Outcome: | The proposed model improves performance on a biomedical information extraction task using expert and lay annotations. |
Detecting Depression in Social Media using Fine-Grained Emotions (N19-1)
Copied to clipboard
| Challenge: | Mental disorders affect millions of people around the world and depression is among the most common. |
| Approach: | They propose a representation of social media documents by a set of emotions generated by lexical resources and subword embeddings. |
| Outcome: | The proposed representation improves the results of the evaluation based on the core emotions and the state-of-the-art representations compared to the current methods. |
A Silver Standard Corpus of Human Phenotype-Gene Relations (N19-1)
Copied to clipboard
| Challenge: | Existing tools for phenotype-gene relations extraction require annotated corpus, which requires manual effort and time. |
| Approach: | They propose to generate a silver standard corpus of human phenotype and gene annotations and their relations using Named-Entity Recognition tools. |
| Outcome: | The proposed corpus was generated with Named-Entity Recognition tools with a precision of 87.01%. |
Improving Lemmatization of Non-Standard Languages with Joint Learning (N19-1)
Copied to clipboard
| Challenge: | Lemmatization is a task of mapping a token to its corresponding dictionary head-form to abstract away from orthographic and inflectional variation. |
| Approach: | They propose to improve lemmatization performance on non-standard historical languages . they propose an Encoder-Decoder architecture which enriches with sentence information . |
| Outcome: | The proposed model does not require POS or morphological annotations, which are not always available for historical corpora. |
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown that contextualized word representations are a viable alternative to simple word prediction tasks. |
| Approach: | They propose to use subword units and characters to model morphology, syntax, and semantics instead of word embeddings. |
| Outcome: | The proposed representations are better for modeling syntax and more robust to noisy input. |
A Simple Joint Model for Improved Contextual Neural Lemmatization (N19-1)
Copied to clipboard
| Challenge: | False positive: a core NLP task of lemmatization seeks to map multiple forms of English verbs to a canonical one, known as the lemma. |
| Approach: | They propose a joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
| Outcome: | The proposed model achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. |
A Probabilistic Generative Model of Linguistic Typology (N19-1)
Copied to clipboard
| Challenge: | a generative model of languages based on principles-and-parameters posits that languages toggle on or off . linguistic typologists use a set of universal parameters to determine which languages toggle . we show that the correlation between parameters is significant, and that it is not enough to write down the set of parameters available to languages. |
| Approach: | They propose a generative model of language based on exponential-family matrix factorisation. |
| Outcome: | a linguistic model outperforms baseline models on predicting held-out features by exploiting similarities between languages and their features. |
Quantifying the morphosyntactic content of Brown Clusters (N19-1)
Copied to clipboard
| Challenge: | Using corpora representing several language families, we show that word clusters are highly capable at distinguishing Parts of Speech. |
| Approach: | They propose to use Brown and Exchange word clusters to represent morphosyntactic information in NLP systems. |
| Outcome: | The proposed clusters are highly capable at distinguishing Parts of Speech and can be used to perform tasks dependent on morphosyntactic information. |
Analyzing Bayesian Crosslingual Transfer in Topic Models (N19-1)
Copied to clipboard
| Challenge: | a theoretical analysis of crosslingual transfer in probabilistic topic models is presented . we use Gibbs sampling to quantify the loss of knowledge across languages . |
| Approach: | They propose a method to quantify the loss of knowledge across languages during crosslingual transfer in probabilistic topic models. |
| Outcome: | The proposed model quantifies the loss of knowledge across languages during this process . it is validated on a diverse set of five languages and discusses best practices for data collection and model design . |
Recursive Subtree Composition in LSTM-Based Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | Existing studies show that tree structure modelling on top of sequence modelling is not feasible. |
| Approach: | They propose to recursively compose subtree representations in a biLSTM-based parser to capture subtreas. |
| Outcome: | The proposed model improves performance under ablating the backward LSTM and the forward LS. |
Cross-lingual CCG Induction (N19-1)
Copied to clipboard
| Challenge: | Combinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire unsupervised. |
| Approach: | They propose an alternative using a source-language parser and a parallel corpus to induce a grammar and parsing model for a target language. |
| Outcome: | The proposed model outperforms POS tags on 3 out of 8 languages and unsupervised CCG induction on 6 out of 8. |
Density Matching for Bilingual Word Embedding (N19-1)
Copied to clipboard
| Challenge: | Recent approaches to cross-lingual word embeddings have been based on linear transformations between the embeddable vectors in the two languages. |
| Approach: | They propose a method that expresses two monolingual embedding spaces as probability densities and matches them using a Gaussian mixture model. |
| Outcome: | The proposed method can achieve competitive or superior performance on bilingual lexicon induction and cross-lingual word similarity data. |
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | Existing methods for multilingual transfer are limited by their dynamic nature. |
| Approach: | They propose a method that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. |
| Outcome: | The proposed method outperforms the state-of-the-art on 6 languages, yielding an improvement of 6.8 LAS points on average. |
Early Rumour Detection (N19-1)
Copied to clipboard
| Challenge: | Existing studies on rumour detection are concerned with timing, but few are interested in how early we can detect them. |
| Approach: | They propose a method that integrates reinforcement learning to learn the minimum number of posts required before classifying an event as a rumour. |
| Outcome: | The proposed model detects rumours earlier than state-of-the-art systems while maintaining comparable accuracy. |
Microblog Hashtag Generation via Encoding Conversation Contexts (N19-1)
Copied to clipboard
| Challenge: | Automated hashtag annotation plays an important role in content understanding for microblog posts. |
| Approach: | They propose to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. |
| Outcome: | The proposed model outperforms existing models on two large-scale datasets . it can generate rare and even unseen hashtags, which is not possible with existing models . |
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems (N19-1)
Copied to clipboard
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych
| Challenge: | Recent studies show that visual similarity can play a decisive role in assessing the meaning of characters. |
| Approach: | They investigate the impact of visual adversarial attacks on current NLP systems . they explore three shielding methods that significantly improve the robustness of the models . |
| Outcome: | The proposed methods improve performance but still fall behind non-attack scenarios. |
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (N19-1)
Copied to clipboard
| Challenge: | Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts. |
| Approach: | They analyze reddit.com data to predict the ultimate controversiality of posts . they use textual content and tree structure of early comments to predict content . |
| Outcome: | The proposed model predicts the ultimate controversiality of posts using features drawn from textual content and tree structure of early comments. |
No Permanent Friends or Enemies: Tracking Relationships between Nations from News (N19-1)
Copied to clipboard
| Challenge: | Understanding complex international relations is important but challenging for civilians . topic models and neural models have been proposed to explore relations without supervision . |
| Approach: | They propose an unsupervised neural model that integrates linguistic insights into the model to infer relations between nations from news articles. |
| Outcome: | The proposed model outperforms baselines from topic models and hidden Markov models. |
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation (N19-1)
Copied to clipboard
| Challenge: | a study of long documents that do not include short sections in their titles shows that they improve comprehension and speed . |
| Approach: | They propose to extract the most salient sentence and apply deletion-based compression to generate section titles in low-resource environments. |
| Outcome: | The proposed approach outperforms other methods in low-resource environments while outperforming other approaches. |
Unifying Human and Statistical Evaluation for Natural Language Generation (N19-1)
Copied to clipboard
| Challenge: | Human evaluation captures quality but fails to capture diversity . statistical evaluation fails to catch models that plagiarize from training set . |
| Approach: | They propose a framework which evaluates both diversity and quality based on the optimal error rate of predicting whether a sentence is human-generated. |
| Outcome: | The proposed framework evaluates diversity and quality on summarization and chit-chat dialogue. |
What makes a good conversation? How controllable attributes affect human judgments (N19-1)
Copied to clipboard
| Challenge: | Existing work on dialogue models for conversational quality is incompletely understanding the relationship between quality and individual attributes. |
| Approach: | They propose to use conditional training and weighted decoding to control four attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. |
| Outcome: | The proposed methods improve human quality judgments by controlling combinations of these variables. |
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search (N19-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder models have been successful at a variety of NLP tasks, including machine translation, parsing, and dialog generation. |
| Approach: | They propose a method for search-aware training via a continuous relaxation of beam search to enable global normalization. |
| Outcome: | The proposed approach is able to train globally normalized recurrent sequence models through simple backpropagation. |
Pun Generation with Surprise (N19-1)
Copied to clipboard
| Challenge: | In this paper, we explore creative generation with a focus on puns. |
| Approach: | They propose an unsupervised approach to generating puns using lots of raw text and a surprisal principle. |
| Outcome: | The proposed approach generates puns 30% of the time, doubles the neural generation baseline. |
Single Document Summarization as Tree Induction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to extractive summarization use recurrent neural networks to model document . Existing systems use a vector representation for each sentence to generate a summary . |
| Approach: | They propose a model that induces a multi-root dependency tree while predicting the output summary. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
Fixed That for You: Generating Contrastive Claims with Semantic Edits (N19-1)
Copied to clipboard
| Challenge: | Understanding contrastive opinions is a key component of argument generation. |
| Approach: | They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view. |
| Outcome: | The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast. |
Box of Lies: Multimodal Deception Detection in Dialogues (N19-1)
Copied to clipboard
| Challenge: | Deception occurs during everyday conversations, but this setting has received little attention from the research community. |
| Approach: | They propose to analyze multimodal deceptive dialogues in a box of lies game . they use facial and linguistic annotations to identify deceptives and truthful behaviors . |
| Outcome: | The proposed model outperforms both a random and a human baseline and achieves up to 69% accuracy in distinguishing deceptive and truthful behaviors. |
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation (N19-1)
Copied to clipboard
| Challenge: | a corpus of anaphoric information (coreference) is crowdsourced through a game-with-a-purpose . its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. |
| Approach: | They propose to crowdsource anaphoric information corpus by a game-with-a-purpose and to use it to train a coreference resolver. |
| Outcome: | The proposed corpus contains annotations for 108,000 markables and 20 judgments per markable, and 2.2M in total. |
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
Copied to clipboard
| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
Unsupervised Dialog Structure Learning (N19-1)
Copied to clipboard
| Challenge: | Current dialog systems require human experts to design the dialog structure, which is time consuming and sometimes insufficient to satisfy various customer needs. |
| Approach: | They propose to extract dialog structure using a modified VRNN model with discrete latent vectors. |
| Outcome: | The proposed model outperforms existing models on the ability to predict unseen data and is faster and more effective in a reinforcement learning setting. |
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
Copied to clipboard
| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation (N19-1)
Copied to clipboard
| Challenge: | Existing methods to learn user and item representations from reviews are limited . existing methods learn user representations based on ratings given by users . |
| Approach: | They propose a hierarchical user and item representation model with three-tier attention to learn user and items from reviews for recommendation. |
| Outcome: | The proposed model can learn user and item representations from reviews on four benchmark datasets. |
Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing (N19-1)
Copied to clipboard
| Challenge: | Existing methods to estimate document similarity based on word embeddings are mediocre . a recent study compared word and sentence embedded documents to a similarity estimate using matrix norms. |
| Approach: | They propose to combine word embeddings with matrix norms to obtain a similarity estimate. |
| Outcome: | The proposed method produces superior results for most of the investigated matrix norms compared to the classical cosine measure and several other similarity estimates. |
Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction (N19-1)
Copied to clipboard
| Challenge: | Graph Convolutional Networks (GCNs) model nodes’ local pairwise importance but lack the capability to model global relative importance in tasks where global ranking is a key component for the task. |
| Approach: | They propose to incorporate global relative importance information into the GCN family of models by using scaled node weights. |
| Outcome: | The proposed method improves keyphrase extraction by 2% and improves the baseline by 5%. |
A Study of Latent Structured Prediction Approaches to Passage Reranking (N19-1)
Copied to clipboard
| Challenge: | a structured output framework is useful for learning to rank problems . current approaches for answer sentence reranking are mostly based on pairwise ranking signals or simple binary classification. |
| Approach: | They propose a structured output approach which regards rankings as latent variables . they propose an inference procedure to find the max-violating ranking based on decomposition of the corresponding loss. |
| Outcome: | The proposed approach solves the optimization problem on WikiQA and TREC13 datasets. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
Tweet Stance Detection Using an Attention based Neural Ensemble Model (N19-1)
Copied to clipboard
| Challenge: | Existing deep learning approaches to stance detection in twitter are inadequate to deal with the vanishing-gradient and overfitting problems. |
| Approach: | They propose a neural ensemble model that adopts strengths of two LSTM variants to learn better long-term dependencies. |
| Outcome: | The proposed model improves on the existing deep learning models on single and multi-target stance detection datasets. |
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information (N19-1)
Copied to clipboard
| Challenge: | Existing evaluation metrics for machine translation are difficult to address word meaning because it is a surface-level metric. |
| Approach: | They propose to use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeds as features, weight, and the distance between two features as features. |
| Outcome: | The proposed metric can evaluate machine translation based on word meaning . it achieves highest correlation with human judgment among several representative metrics. |
Learning to Stop in Structured Prediction for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Beam search optimization solves many problems in neural machine translation, but lacks principled stopping criteria and does not learn how to stop during training. |
| Approach: | They propose a ranking method which enables an optimal beam search stop-ping criteria and a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. |
| Outcome: | Experiments on synthetic and real languages show that the proposed methods improve translation quality and length. |
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs (N19-1)
Copied to clipboard
| Challenge: | Recent research has found that a shared bilingual word embedding space can be induced by projecting monolingual word embeds from two languages without any bilingual supervision. |
| Approach: | They propose a framework for learning unsupervised multilingual word embeddings that mitigates instability issues for distant language pairs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two downstream tasks outperforming even supervised baselines. |
Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
Improving Robustness of Machine Translation with Synthetic Noise (N19-1)
Copied to clipboard
| Challenge: | Recent work on MT robustness has demonstrated the need to build or adapt systems that are resilient to such noise. |
| Approach: | They propose to synthesize natural noise in social media data to enhance robustness of MT systems by leveraging natural noise. |
| Outcome: | The proposed method can make a vanilla MT system more resilient to noise, partially mitigating loss in accuracy resulting therefrom. |
Non-Parametric Adaptation for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural Networks trained with gradient descent are susceptible to catastrophic forgetting due to parameter shift during the training process. |
| Approach: | They propose a semi-parametric approach that relies on local phrase level similarities to retrieve neighboring phrases that are useful for translation even when overall sentence similarity is low. |
| Outcome: | The proposed approach performs well on a heterogeneous dataset with WMT, IWSLT, JRC-Acquis and OpenSubtitles. |
Online Distilling from Checkpoints for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train. |
| Approach: | They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system . |
| Outcome: | The proposed method improves on-the-fly on several datasets and language pairs. |
Value-based Search in Execution Space for Mapping Instructions to Programs (N19-1)
Copied to clipboard
| Challenge: | Existing methods to map instructions to programs require searching for good programs at training time. |
| Approach: | They propose a search algorithm that uses the target world state to train a critic network that predicts the expected reward of every search state. |
| Outcome: | The proposed algorithm significantly improves on all three domains compared to baselines on the SCONE dataset. |
VQD: Visual Query Detection In Natural Scenes (N19-1)
Copied to clipboard
| Challenge: | Existing visual referring expression recognition tasks have multiple annotation problems and language bias problems. |
| Approach: | They propose a visual grounding task called Visual Query Detection . they evaluate the first algorithms on visual referring expression datasets and VQDv1 datasets . |
| Outcome: | The proposed algorithms are compared with existing visual referring expression comprehension datasets and the new VQDv1 dataset. |
Improving Natural Language Interaction with Robots Using Advice (N19-1)
Copied to clipboard
| Challenge: | Recent studies focus on learning models for physically grounded language understanding tasks such as the blocks world domain. |
| Approach: | They propose a protocol for including advice, high-level observations about the task, which can help constrain the agent’s prediction. |
| Outcome: | The proposed approach can be extended to include advice, high-level observations about the task, and reduce the effort involved in supplying the advice. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
Copied to clipboard
| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA (N19-1)
Copied to clipboard
| Challenge: | Existing work on unimodal approaches often lacks dataset biases . we present unimod ablations on three recent datasets in visual navigation and QA . |
| Approach: | They propose unimodal ablations for visual navigation and QA using egocentric vision . they argue that unimodulated models better capture and reflect dataset biases . |
| Outcome: | The proposed models outperform full models on visual navigation and QA tasks with language only on three recent datasets. |
ExCL: Extractive Clip Localization Using Natural Language Descriptions (N19-1)
Copied to clipboard
| Challenge: | Prior approaches to retrieving clips within videos based on a given query are inefficient and text-clip similarity driven ranking-based approaches are far more complicated. |
| Approach: | They propose an extractive approach that extracts the start and end frames by leveraging cross-modal interactions between the text and video to generate a joint representation. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art on two datasets and has comparable performance on a third. |
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus (N19-1)
Copied to clipboard
| Challenge: | Existing methods for automatic detection of Alzheimer's disease (AD) are limited by a lack of data. |
| Approach: | They propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. |
| Outcome: | The proposed method outperforms both unilingual and machine translation-based baselines in Mandarin Chinese and is the first to transfer feature domains in detecting cognitive decline. |
Cross-lingual Visual Verb Sense Disambiguation (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. |
| Approach: | They extend their work to the task of cross-lingual verb sense disambiguation by using a dataset annotated with English, German, and Spanish verbs. |
| Outcome: | The proposed model improves the results of a text-only machine translation system when used for a multimodal translation task. |
Subword-Level Language Identification for Intra-Word Code-Switching (N19-1)
Copied to clipboard
| Challenge: | Code-switching (CS) is a phenomenon of alternating between two or more languages in conversations . if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language. |
| Approach: | They propose to extend the language identification task to the subword level by splitting mixed words while tagging each part with a language ID. |
| Outcome: | The proposed model outperforms the baseline on a Spanish–Wixarika and adapted German–Turkish datasets. |
MuST-C: a Multilingual Speech Translation Corpus (N19-1)
Copied to clipboard
| Challenge: | Current research on spoken language translation (SLT) has to confront the scarcity of sizeable and publicly available training corpora. |
| Approach: | They propose a multilingual speech translation corpus that will facilitate the training of end-to-end systems for SLT from English into 8 languages. |
| Outcome: | The proposed multilingual speech translation corpus will facilitate the training of end-to-end systems for spoken language translation from English into 8 languages. |
Contextualization of Morphological Inflection (N19-1)
Copied to clipboard
| Challenge: | In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. |
| Approach: | They propose a task that requires morphological features to be inferred from sentential context . they propose morphology-based models that explicitly reconstruct morphologic features before predicting inflected forms . |
| Outcome: | The proposed model is able to predict inflected sentences without relying on morphological annotations. |
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
Copied to clipboard
| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
Copied to clipboard
| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
Measuring Immediate Adaptation Performance for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Incremental domain adaptation improves interactive machine translation performance . users of interactive systems are sensitive to the speed of adaptation . |
| Approach: | They propose to measure the speed of lexical acquisition for in-domain vocabulary . they propose to use this to choose the most suitable adaptation method for neural machine translation . |
| Outcome: | The proposed measures measure the speed of lexical acquisition for in-domain vocabulary . they show that the most suitable adaptation method is chosen from a range of different techniques . |
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to correct exposure bias in machine translation are inadequate . scheduled sampling assumes that words are aligned at each time step . |
| Approach: | They propose a differentiable sampling algorithm that optimizes the probability that the reference can be aligned with the sampled output. |
| Outcome: | The proposed approach improves BLEU on translation tasks and is simpler to train with no sampling schedule. |
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | a heterogeneous training dataset can vary in characteristics such as domain, translation quality, and degree of difficulty. |
| Approach: | They propose to use reinforcement learning to learn an optimal curriculum for NMT training . they find it can beat uniform baselines and hand-designed, state-of-the-art curricula . |
| Outcome: | The proposed approach beats baselines and hand-designed curricula on English-to-French datasets. |
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) performs poorly without large training corpora. |
| Approach: | They propose a machine learning method that retains the majority of general-domain performance lost in continued training without degrading in-domain. |
| Outcome: | The proposed method retains the majority of general-domain performance lost in continued training without degrading in-domain performances. |
Short-Term Meaning Shift: A Distributional Exploration (N19-1)
Copied to clipboard
| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
| Outcome: | The proposed model has problems distinguishing meaning shift from referential phenomena, and measures contextual variability to remedy this. |
Detecting Derogatory Compounds – An Unsupervised Approach (N19-1)
Copied to clipboard
| Challenge: | Derogatory compounds are more difficult to detect than derogatory unigrams since they are sparsely represented in general-purpose lexical resources. |
| Approach: | They propose an unsupervised classification approach that incorporates linguistic properties of compounds. |
| Outcome: | The proposed method is compared with existing methods for extracting derogatory unigrams . the proposed method uses a distributional representation to incorporate linguistic properties of compounds . |
Personalized Neural Embeddings for Collaborative Filtering with Text (N19-1)
Copied to clipboard
| Challenge: | Traditional CF approaches exploit user-item relations only and suffer from data sparsity issues. |
| Approach: | They develop a Personalized Neural Embedding framework to exploit both interactions and words seamlessly. |
| Outcome: | The proposed framework exploits both interactions and words seamlessly and predicts user preferences on items based on these embeddings. |
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
Copied to clipboard
| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
| Outcome: | The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty. |
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
Copied to clipboard
| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing studies have attempted to personalize models to improve performance on NLP tasks such as sentiment analysis but they did not estimate subjective input. |
| Approach: | They propose a method of modeling personal biases in word meanings with personalized word embeddings by solving a task on subjective text while regarding words used by different individuals as different words. |
| Outcome: | The proposed method improves sentiment analysis and target task with reviews retrieved from RateBeer. |
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media (N19-1)
Copied to clipboard
| Challenge: | a number of fact-checking initiatives have been launched, both manual and automatic, but the whole enterprise remains in a state of crisis. |
| Approach: | They propose a multi-task ordinal regression framework that models trustworthiness estimation and political ideology detection of entire news outlets. |
| Outcome: | The proposed model outperforms models that target the problems in isolation. |
Joint Detection and Location of English Puns (N19-1)
Copied to clipboard
| Challenge: | Existing research on puns has focused on understanding the meanings of words and phrases. |
| Approach: | They propose a model that addresses pun detection and pun location jointly from a sequence labeling perspective. |
| Outcome: | Empirical results show that the proposed model can handle both homographic and heterographic puns. |
Harry Potter and the Action Prediction Challenge from Natural Language (N19-1)
Copied to clipboard
| Challenge: | Using textual descriptions of scenes, we explore the challenge of action prediction from textual description. |
| Approach: | They propose a testbed to approximate whether text inference can be used to predict upcoming actions from textual descriptions of scenes. |
| Outcome: | The proposed model performs best for frequent actions and large scene descriptions, but logistic regression fails on infrequent actions. |
Argument Mining for Understanding Peer Reviews (N19-1)
Copied to clipboard
| Challenge: | In 2015 alone, approximately 63.4 million hours were spent on peer reviews. |
| Approach: | They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types. |
| Outcome: | The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). |
An annotated dataset of literary entities (N19-1)
Copied to clipboard
| Challenge: | Existing datasets built on news focus on non-named entities, but not literary texts. |
| Approach: | They propose to annotate 210,532 tokens from 100 different English-language literary texts for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). |
| Outcome: | The proposed dataset includes 210,532 tokens drawn from 100 different English-language literary texts. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (N19-1)
Copied to clipboard
| Challenge: | Sense representations target meaning conflation deficiency but their potential impact has not been investigated in downstream NLP applications. |
| Approach: | They propose to use a reverse dictionary system to address meaning conflation deficiency . they propose to integrate senses into the system to improve semantic understanding . |
| Outcome: | The proposed approach can improve the performance of a downstream NLP application. |
Factorising AMR generation through syntax (N19-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
| Approach: | They propose to decompose the generation process into two steps: first generate a syntactic structure, and then generate the surface form. |
| Outcome: | The proposed approach generates meaning-preserving syntactic paraphrases of the same graph, as judged by humans. |
A Crowdsourced Frame Disambiguation Corpus with Ambiguity (N19-1)
Copied to clipboard
| Challenge: | Using crowdsourcing, we have found that inter-annotator disagreement is at least partly caused by ambiguity inherent to the text and frames. |
| Approach: | They propose a crowdsourcing approach to capture inter-annotator disagreement by a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. |
| Outcome: | The proposed approach captures disagreement between the annotations of 1,000 word-sentence pairs and scores on the likelihood that each frame applies to the word. |
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets (N19-1)
Copied to clipboard
| Challenge: | Several datasets have been constructed to expose brittleness in models trained on existing benchmarks. |
| Approach: | They propose to use a challenge dataset to examine model adaptations by exposing models to a metaphorical pathogen and assessing how well they can adapt. |
| Outcome: | The proposed method analyzes the NLI stress tests and the Adversarial SQuAD datasets and shows that they are no longer challenging and others remain difficult. |
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (N19-1)
Copied to clipboard
| Challenge: | Existing knowledge graphs with billions of triples are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose to embed relationship triples into a capsule network using a convolution layer and multiple filters to generate feature maps. |
| Outcome: | The proposed model outperforms strong search personalization baselines on two benchmark datasets and outperformed previous state-of-the-art models on WN18RR and FB15k-237. |
Partial Or Complete, That’s The Question (N19-1)
Copied to clipboard
| Challenge: | Existing annotation schemes aim at acquiring completely annotated structures, but partial annotations can be costly and hinder learning. |
| Approach: | They propose a method to find out that learning from partial structures can sometimes outperform learning from complete ones. |
| Outcome: | The proposed method outperforms existing methods in three different structured learning tasks. |
Sequential Attention with Keyword Mask Model for Community-based Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing methods to model answer selection(AS) are based on feature engineering and resource toolkits. |
| Approach: | They propose a model that captures features and information from question and answer text and repeats multiple times(hops) in a sequential fashion. |
| Outcome: | The proposed model performs on answer selection tasks and multi-level answer ranking tasks. |
Simple Attention-Based Representation Learning for Ranking Short Social Media Posts (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to ranking short social media posts are complex and require different components to capture a multitude of relevance signals. |
| Approach: | They propose a word-level Siamese architecture with attention-based mechanisms for capturing semantic "soft" matches between query and post tokens. |
| Outcome: | The proposed model is faster and simpler than existing models and more efficient than existing approaches. |
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification (N19-1)
Copied to clipboard
| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
| Approach: | They propose a task-agnostic pipelined system that extracts textual evidence that supports or refutes a factual claim from Wikipedia pages. |
| Outcome: | The proposed system achieves state-of-the-art results on the FEVER dataset. |
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities (N19-1)
Copied to clipboard
Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, Marie-Catherine de Marneffe
| Challenge: | Scholars in interdisciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. |
| Approach: | They propose an active learning solution for named entity recognition that maximizes a custom model’s improvement per additional unit of manual annotation. |
| Outcome: | The proposed model reduces required annotation by 20-60% and outperforms a competitive active learning baseline. |
Doc2hash: Learning Discrete Latent variables for Documents Retrieval (N19-1)
Copied to clipboard
| Challenge: | Learning to hash via generative model is a powerful paradigm for fast similarity search in documents retrieval. |
| Approach: | They propose a method that trains a generative model to generate hash codes by using continuous relaxation on priors. |
| Outcome: | The proposed method outperforms other state-of-the-art methods in qualitative and quantitative experiments. |
Evaluating Text GANs as Language Models (N19-1)
Copied to clipboard
| Challenge: | Generative Adversarial Networks (GANs) do not suffer from the problem of exposure bias. |
| Approach: | They propose to approximate the distribution of text generated by a GAN and compare it to traditional probability-based LM metrics. |
| Outcome: | The proposed method performs significantly worse than state-of-the-art LMs on several GAN-based models and can accelerate progress in GAN text generation. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
Copied to clipboard
| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |
Neural Text Generation from Rich Semantic Representations (N19-1)
Copied to clipboard
| Challenge: | 2 is a neural model that maps a linearization of Dependency MRS to text . 1 is based on a BLEU score of 66.11 when trained on gold data . |
| Approach: | They propose to use Minimal Recursion Semantics to generate high-quality text from structured representations. |
| Outcome: | The proposed model achieves a BLEU score of 77.17 on the full test set and 83.37 on the subset of test data most closely matching the silver data domain. |
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (N19-1)
Copied to clipboard
| Challenge: | Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. |
| Approach: | They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. |
| Outcome: | The proposed method improves reliability and adequacy while maintaining fluent output. |
Evaluating Rewards for Question Generation Models (N19-1)
Copied to clipboard
| Challenge: | Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. |
| Approach: | They propose to use a Seq2Seq architecture to train models to generate one-step-ahead predictions, but at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process. |
| Outcome: | The proposed model is trained to generate a plausible question, conditioned on an input document and answer span within that document. |
Text Generation from Knowledge Graphs with Graph Transformers (N19-1)
Copied to clipboard
| Challenge: | Existing methods for generating text with structured inputs are expensive and require manual annotation. |
| Approach: | They propose a graph transforming encoder which leverages relational structure of knowledge graphs without imposing linearization or hierarchical constraints. |
| Outcome: | The proposed system produces more informative texts than competing methods. |
Open Information Extraction from Question-Answer Pairs (N19-1)
Copied to clipboard
| Challenge: | Existing work on OpenIE extracts structured data from sentences . a system for extracting tuples from question-answer pairs solves this problem . |
| Approach: | They propose a system for extracting tuples from question-answer pairs . they use distributed representations of a question and an answer to generate knowledge facts . |
| Outcome: | The proposed system extracts meaningful structured tuples from question-answer pairs . it can find new and interesting facts to extend knowledge bases, the authors show . |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
Copied to clipboard
| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC (N19-1)
Copied to clipboard
| Challenge: | In response to this development, there have been a flurry of new datasets for question answering. |
| Approach: | They propose to use SQuAD 2.0, QuAC, and CoQA to provide question answering on textual data. |
| Outcome: | The proposed datasets provide complementary coverage of the first two aspects, but weak coverage of third. |
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)
Copied to clipboard
| Challenge: | Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce . |
| Approach: | They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions. |
| Outcome: | The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks . |
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text (N19-1)
Copied to clipboard
Ahmad Sakor, Isaiah Onando Mulang’, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, Sören Auer
| Challenge: | Short texts challenge NLP tasks because they lack context or are partially malformed. |
| Approach: | They propose a method which maps entities and relations within a short text to Wikipedia mentions. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for short text query inventories. |
Be Consistent! Improving Procedural Text Comprehension using Label Consistency (N19-1)
Copied to clipboard
| Challenge: | Existing systems for procedural text comprehension still struggle with this task . evaluative work shows that consistent predictions from multiple entities can improve performance . |
| Approach: | They propose a framework that leverages label consistency during training to improve prediction performance. |
| Outcome: | The proposed framework significantly improves prediction performance over previous state-of-the-art systems on a standard benchmark dataset for procedural text, ProPara. |
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
Copied to clipboard
| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
| Approach: | They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver . |
| Outcome: | The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset. |
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs (N19-1)
Copied to clipboard
| Challenge: | a large body of work has highlighted the brittleness of reading comprehension systems . a crowdsourced, adversarially-created, 55k-question benchmark requires a more comprehensive understanding of paragraphs . |
| Approach: | They propose a reading comprehension benchmark that requires Discrete Reasoning over the content of paragraphs. |
| Outcome: | The proposed benchmarks show that the best systems only achieve 38.4% F1 on the generalized accuracy metric, while human performance is 96%. |
An Encoding Strategy Based Word-Character LSTM for Chinese NER (N19-1)
Copied to clipboard
| Challenge: | Existing word-based model can not be trained in batches due to its DAG structure. |
| Approach: | They propose a lattice model that integrates word information into the start or end characters of a word and integrates it into a fixed-sized representation for efficient batch training. |
| Outcome: | The proposed model outperforms other state-of-the-art models on benchmark datasets and shows that it can be trained in batches without a shortcut path. |
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling (N19-1)
Copied to clipboard
| Challenge: | Arabic text is written without short vowels (or diacritics) their presence is essential for properly verbalizing Arabic . |
| Approach: | They propose a character-level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. |
| Outcome: | The proposed model outperforms all previous state-of-the-art models on overlapping windows of words . it achieves a word error rate (WER) of 4.49% compared to the state- of-the art systems . |
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)
Copied to clipboard
| Challenge: | Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task . |
| Approach: | They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information. |
| Outcome: | The proposed model can learn from all tasks and task-specific parameters. |
Learning to Denoise Distantly-Labeled Data for Entity Typing (N19-1)
Copied to clipboard
| Challenge: | Distantly-labeled data can be used to scale up statistical models, but it is noisy . specialized probabilistic models can be employed to scale the training of models, however, they require sophisticated probabilistic inference for the training. |
| Approach: | They propose a method for denoising and denoising noisy data with supervised training. |
| Outcome: | The proposed method outperforms models trained on clean and denoised data on an ultra-fine entity typing task. |
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors (N19-1)
Copied to clipboard
| Challenge: | a recent study shows that neural sequential labelers overfit their training data to detect SVA errors. |
| Approach: | They propose a simple protocol that generates a neural sequential labeler from silver standard data and gold standard data. |
| Outcome: | The proposed method leads to more robust detection of SVA errors on silver standard data and gold standard data. |
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
Copied to clipboard
| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | Existing studies on crosslingual transfer have focused on word-level information sharing, but words are not independent in sentences; their combinations form larger linguistic units, known as context. |
| Approach: | They propose to use orderagnostic models to transfer word order to distant languages . they train dependency parsers on an English corpus and evaluate their transfer performance on 30 other languages. |
| Outcome: | The proposed model performs better on languages with different word orders than on other languages. |
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations (N19-1)
Copied to clipboard
| Challenge: | Empirically, the model with the best performing syntactic and semantic representations gives rise to the most disentangled representations. |
| Approach: | They propose a generative model that uses latent variables to learn a sentence that uses both latent and latent representations. |
| Outcome: | The proposed model achieves better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. |
Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
Copied to clipboard
| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
| Outcome: | The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting. |
Adaptive Convolution for Text Classification (N19-1)
Copied to clipboard
| Challenge: | Existing convolutional neural networks (CNNs) use sparse representations of text, such as bag-of-words. |
| Approach: | They propose an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs) they attach filter-generating networks to convevolution blocks in existing CNNs . |
| Outcome: | The proposed convolution improves performance in seven benchmark datasets by 2.6 percentage points . the proposed conversions can be likened to players of the twenty questions . |
Zero-Shot Cross-Lingual Opinion Target Extraction (N19-1)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis involves the recognition of opinion target expressions . supervised learning algorithms are usually employed to extract OTEs from text . |
| Approach: | They propose a zero-shot cross-lingual approach for the extraction of opinion target expressions . they leverage multilingual word embeddings that share a common vector space across languages . |
| Outcome: | The proposed approach can perform accurate prediction on a target language without using annotated samples. |
Adversarial Category Alignment Network for Cross-domain Sentiment Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods for cross-domain sentiment classification focus on aligning marginal distribution without taking category-specific decision boundaries into consideration. |
| Approach: | They propose an adversarial category alignment network to enhance category consistency . experimental results show the proposed method can achieve state-of-the-art performance . |
| Outcome: | The proposed method achieves state-of-the-art performance and produces more discriminative features on benchmark datasets. |
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)
Copied to clipboard
| Challenge: | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). |
| Approach: | They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target. |
| Outcome: | The proposed model outperforms existing methods significantly on several popular ABSA benchmarks. |
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (N19-1)
Copied to clipboard
| Challenge: | Abstractive summarization methods suffer from inferior performance compared to extractive methods. |
| Approach: | They propose a reddit TIFU dataset and a new abstractive summarization model . they use multi-level memory networks to store information from different levels of abstraction . |
| Outcome: | The proposed model outperforms state-of-the-art summarization models with multi-level memory . the proposed dataset is highly abstractive and outperformed existing models with the proposed model . |
Automatic learner summary assessment for reading comprehension (N19-1)
Copied to clipboard
| Challenge: | Summarization is a well-established method of measuring reading proficiency in traditional English as a second or other language assessments. |
| Approach: | They propose three approaches to automatically assess learner summary for evaluating non-native reading comprehension using a summarization task and a long-term memory model. |
| Outcome: | The proposed models outperform traditional methods and produce quality assessments close to professional examiners. |
Data-efficient Neural Text Compression with Interactive Learning (N19-1)
Copied to clipboard
| Challenge: | Neural sequence-to-sequence models have been successfully applied to text compression, but they are limited to a few domains and tasks. |
| Approach: | They propose a novel setup to neural text compression that enables transferring a model to new domains and compression tasks with minimal human supervision. |
| Outcome: | The proposed setup adapts a model trained on small datasets to a general text compression dataset with just 500 sampled instances annotated by a human. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
Copied to clipboard
| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |
Guiding Extractive Summarization with Question-Answering Rewards (N19-1)
Copied to clipboard
| Challenge: | a primary challenge faced by extractive summarization systems is the lack of annotated data. |
| Approach: | They propose a supervised extractive summarization system that rewards question-answering by identifying salient sequences of words from a document and highlighting them in the text. |
| Outcome: | The proposed system compares with baselines of strong summarization and human assessors on question-answering. |
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat (N19-1)
Copied to clipboard
Ravi Shekhar, Aashish Venkatesh, Tim Baumgärtner, Elia Bruni, Barbara Plank, Raffaella Bernardi, Raquel Fernández
| Challenge: | Existing systems that address the abilities that need to be put to work during conversations are lacking in terms of visual grounding. |
| Approach: | They propose a visually-grounded dialogue state encoder which integrates visual grounding with dialogue system components. |
| Outcome: | The proposed system improves the GuessWhat?! game by combining guessing and asking questions with multi-task learning. |
The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding (N19-1)
Copied to clipboard
| Challenge: | Visual Dialog is a multi-modal task that requires a model to participate in a dialog grounded on an image and generate correct, human-like responses. |
| Approach: | They propose a framework for effective and robust auxiliary training of visual dialog systems using multi-modal encoding. |
| Outcome: | The proposed framework outperforms supervised learning baselines and fine-tuning methods on most metrics of VisDial v0.5/v0.9 generative tasks. |
Strong and Simple Baselines for Multimodal Utterance Embeddings (N19-1)
Copied to clipboard
| Challenge: | Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. |
| Approach: | They propose two simple but strong baselines to learn embeddings of multimodal utterances by factorizing the utterant into unimodal factors. |
| Outcome: | The proposed models show that they can be derived in closed form while maintaining simplicity and efficiency during learning and inference. |
Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout (N19-1)
Copied to clipboard
| Challenge: | Existing approaches perform significantly worse in unseen environments compared to seen ones. |
| Approach: | They propose to use a ‘environmental dropout’ method to generate unseen triplets to generate new paths and instructions to generalize the agent. |
| Outcome: | The proposed agent outperforms the state-of-the-art approaches on the private unseen test set and is ranked top on the leaderboard. |
Towards Content Transfer through Grounded Text Generation (N19-1)
Copied to clipboard
| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
| Outcome: | The proposed task is based on 640k Wikipedia referenced sentences paired with the source articles to show significant improvements against baselines. |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
Copied to clipboard
| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension (N19-1)
Copied to clipboard
| Challenge: | Existing models for Machine Reading Comprehension (MRC) are small, compared to their size, and there are many studies on using pre-trained word embeddings and back-translation approaches to improve model generalization. |
| Approach: | They propose a multi-task learning framework to learn a machine reading comprehension model that can be applied to a wide range of MRC tasks in different domains. |
| Outcome: | The proposed model can be applied to a wide range of MRC tasks in different domains. |
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)
Copied to clipboard
| Challenge: | Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions. |
| Approach: | They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. |
| Outcome: | The proposed model outperforms state-of-the-art models and the best non-retrieval-based models over 10% accuracy in a Math23K dataset. |
Iterative Search for Weakly Supervised Semantic Parsing (N19-1)
Copied to clipboard
| Challenge: | Recent work has focused on training semantic parsers via weak supervision from denotations alone. |
| Approach: | They propose an iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. |
| Outcome: | The proposed algorithm outperforms the previous best systems on WikiTableQuestions and Cornell Natural Language Visual Reasoning (NLVR) iteratively train models that provide guidance to subsequent models to search for logical forms of increasing complexity, thus dealing with spuriousness. |
Alignment over Heterogeneous Embeddings for Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing approaches for non-factoid question answering are based on heterogeneous embeddings that model text at different levels of abstraction. |
| Approach: | They propose a fast, mostly-unsupervised approach for non-factoid question answering called Alignment over Heterogeneous Embeddings (AHE) it aligns each word in the question and candidate answer with the most similar word in retrieved supporting paragraph and a meta-classifier that learns how much to trust the predictions over each representation. |
| Outcome: | The proposed approach outperforms other supervised approaches on the AI2 Reasoning Challenge dataset and the WikiQA dataset. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Incorporating Word Attention into Character-Based Word Segmentation (N19-1)
Copied to clipboard
Shohei Higashiyama, Masao Utiyama, Eiichiro Sumita, Masao Ideuchi, Yoshiaki Oida, Yohei Sakamoto, Isaac Okada
| Challenge: | Word segmentation models are used to minimize the effort in feature engineering. |
| Approach: | They propose a character-based model that learns the importance of multiple candidate words for a corresponding character on the basis of an attention mechanism and makes use of it for segmentation decisions. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Japanese and Chinese benchmark datasets. |
VCWE: Visual Character-Enhanced Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Currently, word embeddings are playing a pivotal role in many natural language processing tasks. |
| Approach: | They propose a model to learn Chinese word embeddings via three-level composition . they use convolutional neural network to extract intra-character compositionality from character shape . |
| Outcome: | The proposed model performs better on word similarity, sentiment analysis, named entity recognition and part-of-speech tagging tasks. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
Copied to clipboard
| Challenge: | Using subwords, Chinese word segmentation models use character combination information to disambiguate characters. |
| Approach: | They propose a subword-based neural word segmentor that integrates character embeddings into a Lattice LSTM network over a character sequence. |
| Outcome: | The proposed model can utilize abundant character combination information, which is effective to disambiguate characters. |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)
Copied to clipboard
| Challenge: | Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words. |
| Approach: | They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations. |
| Outcome: | The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset. |
Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning (N19-1)
Copied to clipboard
| Challenge: | Modern neural morphological analyzers consume gigabytes of memory. |
| Approach: | They propose a method which uses unigram character embeddings to train a model on labels produced by a state-of-the-art analyzer. |
| Outcome: | The proposed model outperforms dictionary-based methods in Japanese and Chinese . it uses less than 15 megabytes of space and is much smaller than the dictionary- based one . |
Neural Constituency Parsing of Speech Transcripts (N19-1)
Copied to clipboard
| Challenge: | a neural parser for transcribed speech can find EDITED disfluency nodes . this makes specialized mechanisms for parsing disfluencies unnecessary . |
| Approach: | They propose a neural self-attentive parser that finds EDITED disfluency nodes in transcribed speech. |
| Outcome: | The proposed parser finds EDITED disfluency nodes with an accuracy surpassing that of specialized systems. |
Acoustic-to-Word Models with Conversational Context Information (N19-1)
Copied to clipboard
| Challenge: | Existing speech recognition models are built at a sentence level, and therefore it may not capture conversational context information. |
| Approach: | They propose a direct acoustic-to-word, end-to end speech recognition model that integrates a conversational context with other available information and directly recognizes words from speech. |
| Outcome: | The proposed model outperforms a standard end-to-end speech recognition system on the Switchboard conversational speech corpus and shows that it is more accurate than existing models. |
A Dynamic Speaker Model for Conversational Interactions (N19-1)
Copied to clipboard
| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
Fluent Translations from Disfluent Speech in End-to-End Speech Translation (N19-1)
Copied to clipboard
| Challenge: | Disfluency removal is an intermediate step between speech recognition and machine translation (MT) with the rise of end-to-end speech translation systems, disfluency recognition and removal needs to be incorporated into the model architectures or handled as a post-processing step. |
| Approach: | They propose to use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
| Outcome: | The proposed model generates fluent translations from disfluent speech using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (N19-1)
Copied to clipboard
| Challenge: | Recent work on relation classification has gained much success by exploiting deep neural networks. |
| Approach: | They propose a relation classification model using Segment-level Attention-based Convolutional Neural Networks and Dependency-based Recurrent Neural networks. |
| Outcome: | The proposed model is comparable to the state-of-the-art without external lexical features on the SemEval-2010 dataset. |
Document-Level Event Factuality Identification via Adversarial Neural Network (N19-1)
Copied to clipboard
| Challenge: | Document-level event factuality identification is crucial for discourse understanding in NLP . identifying document-level factual of events requires comprehensive understanding of documents . |
| Approach: | They propose to construct a corpus annotated with document- and sentence-level event factuality information on English and Chinese texts. |
| Outcome: | The proposed model outperforms baselines on the constructed corpus. |
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (N19-1)
Copied to clipboard
| Challenge: | Existing methods to extract relational data generated by distant supervision generate noisy training data. |
| Approach: | They propose a neural relation extraction method to deal with noisy training data generated by distant supervision. |
| Outcome: | Experimental results show that the proposed method is more accurate than state-of-the-art methods on the New York Times dataset. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train unbiased methods such as REINFORCE take time to train. |
| Approach: | They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets. |
| Outcome: | The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training. |
Scalable Collapsed Inference for High-Dimensional Topic Models (N19-1)
Copied to clipboard
| Challenge: | Existing methods have achieved two out of three criteria simultaneously, but never all three at once. |
| Approach: | They propose an online inference algorithm which leverages stochasticity to scale well in the number of documents and sparsity to achieve accurate inference. |
| Outcome: | The proposed algorithm scales well in the number of documents and topics while achieving accurate inference. |
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document. |
| Approach: | They propose a multi-task learning framework that jointly learns an extractive model and a generative model. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks. |
Predicting Malware Attributes from Cybersecurity Texts (N19-1)
Copied to clipboard
| Challenge: | a new feature learning method is proposed to automatically assign malware attribute labels based on cybersecurity texts. |
| Approach: | They propose a feature learning method to leverage diverse knowledge sources to automatically assign malware attribute labels based on cybersecurity texts. |
| Outcome: | The proposed method outperforms the state-of-the-art malware attribute prediction systems. |
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
Copied to clipboard
| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks (N19-1)
Copied to clipboard
| Challenge: | Existing models that estimate annotators' reliability only consider binary labels and multi-class labels. |
| Approach: | They propose an unsupervised model which can handle binary and multi-class labels and integrate neural networks to model the dependency between latent variables and instances. |
| Outcome: | The proposed model can handle binary and multi-class labels and can estimate reliability of annotators across instances. |
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations (N19-1)
Copied to clipboard
| Challenge: | Several methods have been proposed for automatic music style classification, but they are limited in two aspects. |
| Approach: | They propose a deep learning approach to automatically learn and exploit style correlations by reviewing music reviews on websites. |
| Outcome: | The proposed approach performs well in capturing style correlations. |
Fact Discovery from Knowledge Base via Facet Decomposition (N19-1)
Copied to clipboard
| Challenge: | Recent years have witnessed the emergence and growth of many large-scale knowledge bases (KBs) however, there are some issues unsettled towards enriching the KBs. |
| Approach: | They propose a framework that decomposes the discovery problem into several facet components and an auto-encoder component to estimate some facets of the fact. |
| Outcome: | The proposed framework achieves promising results on a benchmark dataset. |
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to extract relationship between entities in sentences suffer from missing or redundant information. |
| Approach: | They propose a deep neural model that combines the advantages of the two approaches to extract the relationship between two entities in a sentence. |
| Outcome: | The proposed model outperforms baseline models on the SemEval-2010 dataset. |
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases (N19-1)
Copied to clipboard
| Challenge: | Existing methods for knowledge base question answering ignore subtle inter-relationships between the question and the KB. |
| Approach: | They propose to model the two-way flow of interactions between questions and KBs using a bidirectional attentive memory network. |
| Outcome: | The proposed method outperforms existing methods on the WebQuestions benchmark and offers better interpretability compared to baselines. |
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (N19-1)
Copied to clipboard
| Challenge: | In this paper we build a reading comprehension dataset of yes/no questions that are naturally occurring . they often query for complex, non-factoid information, and require difficult entailment-like inference to solve. |
| Approach: | They build a reading comprehension dataset of yes/no questions that are naturally occurring . they find they are unexpectedly challenging and require difficult inferences to solve . |
| Outcome: | The proposed method achieves 80.4% accuracy compared to 90% accuracy of human annotators and 62% majority-baseline. |
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing Key-value Memory Neural Networks are effective for shallow reasoning over documents . but extending them to Knowledge Based Question Answering is not trivial . |
| Approach: | They propose a mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. |
| Outcome: | The proposed solution provides better reasoning abilities on complex questions and achieves state-of-the-art performance. |
Repurposing Entailment for Multi-Hop Question Answering Tasks (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to use entailment models for question answering are limited . large scale datasets are typically framed at a sentence level, whereas question answering requires verifying whether multiple sentences, taken together as a premise, entitle a hypothesis. |
| Approach: | They propose a general architecture that can use entailment models for multi-hop QA tasks. |
| Outcome: | The proposed model outperforms QA models trained on target datasets and the OpenAI transformer models. |
GenderQuant: Quantifying Mention-Level Genderedness (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to detect gendered language require considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features. |
| Approach: | They use existing NLP pipelines to automatically annotate gender of mentions in the text and train a supervised classifier to predict the gender of any mention from its context and evaluate it on unseen text. |
| Outcome: | The proposed method can detect gendered language on movie summaries, movie reviews, news articles, and fiction novels. |
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)
Copied to clipboard
| Challenge: | a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language. |
| Approach: | They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings . |
| Outcome: | The proposed framework generates more cohesive topics than traditional models. |
Learning to Decipher Hate Symbols (N19-1)
Copied to clipboard
| Challenge: | Existing computational models of hate speech focus on a binary or multiclass classification task . a recent study shows an alarming 4.6% increase in hate speech in 2016 . |
| Approach: | They propose a task of deciphering hate symbols using the Urban Dictionary . they propose ciphers using Sequence-to-Sequence models and a Variational Decipher . |
| Outcome: | The proposed model can crack hate symbols based on context and generalize better to unseen symbols in a more challenging testing setting. |
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)
Copied to clipboard
| Challenge: | Existing distance supervised relation extraction models for long-tail data are inadequate for many applications. |
| Approach: | They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks. |
| Outcome: | The proposed approach outperforms baselines for long-tail relations on a large-scale dataset. |
GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
A general framework for information extraction using dynamic span graphs (N19-1)
Copied to clipboard
| Challenge: | Existing frameworks for information extraction use a pipeline approach to identify entities and then use the detected entity spans for relation extraction and coreference resolution. |
| Approach: | They propose a framework for several information extraction tasks that share span representations using dynamically constructed span graphs. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. |
OpenCeres: When Open Information Extraction Meets the Semi-Structured Web (N19-1)
Copied to clipboard
| Challenge: | Open Information Extraction (OpenIE) is a problem of extracting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology. |
| Approach: | They propose an open-source method to extract triples from semi-structured websites . they use a semi-supervised label propagation technique to create training data for relations . |
| Outcome: | The proposed method extracts over 2 million triples from 31 websites in the movie vertical. |
Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
| Approach: | They propose a method that combines the benefits of learning representations and structured learning to predict sentence-level relation mentions given only proposition-level supervision from a KB. |
| Outcome: | The proposed approach outperforms a number of baseline approaches while minimizing label noise. |
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations. |
| Approach: | They propose a method to explicitly model out-of-domain information in an encoder-decoder framework . they propose combining out- of-domain training data with out-out-of domain data . |
| Outcome: | The proposed method outperforms baselines on multiple data sets. |
Selective Attention for Context-aware Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Recent work in context-aware NMT considers only a few previous sentences as context . current systems fail to achieve fluent, good quality translation for a full document . |
| Approach: | They propose a top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context. |
| Outcome: | The proposed approach outperforms context-agnostic baselines and context-based baselines on English-German datasets. |
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models (N19-1)
Copied to clipboard
| Challenge: | Existing methods for assessing the robustness of sequence-to-sequence models have been ignored by the literature. |
| Approach: | They propose an evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. |
| Outcome: | The proposed framework breaks the assumption that source perturbations should not result in changes in the expected output, but allows for meaning-preserving perturbations that change the output sequence. |
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction (N19-1)
Copied to clipboard
| Challenge: | In reinforcement learning-based sentence generation, the large action space is often too computa-tionally demanding to be used with large training data. |
| Approach: | They propose to reduce the action space by using dynamic vocabulary prediction to generate a fixed-size small vocabulary for each input to generate its target sentence. |
| Outcome: | The proposed method achieves faster reinforcement learning (2.7x faster) with less GPU memory (2.3x less) and more rewards with fewer iterations of supervised pre-training. |
Mitigating Uncertainty in Document Classification (N19-1)
Copied to clipboard
| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification (N19-1)
Copied to clipboard
Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
| Challenge: | Recent research has applied sequence-to-sequence (Seq2Sequen) models to text simplification . generic models tend to copy directly from the original sentence, resulting in outputs that are long and complex. |
| Approach: | They propose to incorporate word complexities into the loss function during training and generate a large set of diverse candidate simplifications at test time. |
| Outcome: | The proposed model can perform competitively with state-of-the-art systems while generating simpler sentences. |
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture (N19-1)
Copied to clipboard
| Challenge: | Unlike Community Question Answering, where questions are mostly factoid based, forum threads are often open-ended and contain repetitive or irrelevant posts. |
| Approach: | They propose a recurrent neural network-based architecture to model the relevance of a post regarding the original post starting the thread and the novelty it brings to the discussion. |
| Outcome: | The proposed model outperforms the state-of-the-art models for text classification on different types of online forum datasets. |
Text Classification with Few Examples using Controlled Generalization (N19-1)
Copied to clipboard
| Challenge: | Current training data for text classification is limited, resulting in limited generalization capacity. |
| Approach: | They propose a feed-forward network that can generalize from unlabeled parsed corpora to produce task-specific semantic vectors. |
| Outcome: | The proposed approach is especially effective in low-data scenarios compared to state-of-the-art methods. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
Copied to clipboard
| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to multi-label classification are based on pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. |
| Approach: | They propose a new training objective that maximizes this set probability and a prediction objective that finds the most probable set on a test document. |
| Outcome: | The proposed model outperforms existing methods on a set of labels for multi-label classification . the proposed model is based on 'set probability' and 'prediction objective' |
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language (N19-1)
Copied to clipboard
Sudipta Saha Shubha, Nafis Sadeq, Shafayat Ahmed, Md. Nahidul Islam, Muhammad Abdullah Adnan, Md. Yasin Ali Khan, Mohammad Zuberul Islam
| Challenge: | Existing methods for Grapheme to phoneme conversion in Bangla language are mostly rule-based. |
| Approach: | They propose to use a lexicon to train a robust Grapheme to phoneme conversion system in Bangla language. |
| Outcome: | The proposed method outperforms other state-of-the-art approaches for G2P conversion in Bangla language. |
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
| Approach: | They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase . |
| Outcome: | The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework. |
Segmentation-free compositional n-gram embedding (N19-1)
Copied to clipboard
| Challenge: | Existing word embedding models depend on word segmentation, but this method is difficult when corpora written in noisy or unsegmented languages. |
| Approach: | They propose a new method that models words, phrases and sentences seamlessly without word segmentation. |
| Outcome: | The proposed method is very effective for noisy corpora written in unsegmented languages such as Chinese and Japanese. |
Exploiting Noisy Data in Distant Supervision Relation Classification (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to relation classification are noisy and time-consuming . RCEND uses noisy data to split noisy data into correctly and incorrectly labeled data . |
| Approach: | They propose a framework to enhance relation classification by exploiting noisy data . they use an instance discriminator with reinforcement learning to split noisy data into correctly and incorrectly labeled data based on the noisy data. |
| Outcome: | The proposed method outperforms the state-of-the-art models on relation classification . the proposed method is based on a semi-supervised learning method . |
Misspelling Oblivious Word Embeddings (N19-1)
Copied to clipboard
Aleksandra Piktus, Necati Bora Edizel, Piotr Bojanowski, Edouard Grave, Rui Ferreira, Fabrizio Silvestri
| Challenge: | Existing word embeddings have limited applicability to malformed texts . misspellings are frequent and embeddable for words that have not been observed at training time . |
| Approach: | They propose a method to learn word embeddings that are resilient to misspellings . they use FastText with subwords to train embeddables on a new dataset . |
| Outcome: | The proposed method is tested on a publicly available dataset. |
Learning Relational Representations by Analogy using Hierarchical Siamese Networks (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to learn representations of relations by textual mentions require a large amount of examples for each relation to reach satisfactory performance. |
| Approach: | They propose a method to learn representations of relations expressed by their textual mentions by matching triples in knowledge bases with web-scale corpora through distant supervision. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on a relation extraction task. |
An Effective Label Noise Model for DNN Text Classification (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train deep neural networks with label noise are limited to image classification models . label noise is important because of the large number of errors and errors in training datasets . |
| Approach: | They propose a non-linear processing layer that models label noise into a convolutional neural network (CNN) they add a noise model layer on top of their target model to account for label noise . |
| Outcome: | The proposed approach is robust to label noise and can learn better sentences . it is based on extensive experiments on text classification datasets . |
Understanding Learning Dynamics Of Language Models with SVCCA (N19-1)
Copied to clipboard
| Challenge: | a new study shows that neural models implicitly encode linguistic features . but no research shows how these encodings arise as the models are trained . |
| Approach: | They propose a method that compares learning across time and across models using annotated data. |
| Outcome: | The proposed method compares learned representations across time and across models without evaluation on annotated data. |
Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models (N19-1)
Copied to clipboard
| Challenge: | a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus is effective, and more time-efficient than training on ngrams. |
| Approach: | They propose a technique that uses large corpus n-gram statistics as a regularizer for training on a smaller corpus. |
| Outcome: | The proposed technique is effective and more time-efficient than training on a larger corpus. |
Continual Learning for Sentence Representations Using Conceptors (N19-1)
Copied to clipboard
| Challenge: | Existing sentence encoders for distributed representations of sentences are limited in their performance on fixed corpora. |
| Approach: | They propose a continual learning scenario for distributed representations of sentences . they initialize sentence encoders with corpus-independent features and update them sequentially . |
| Outcome: | The proposed sentence encoder can learn features from new corpora while maintaining its competence on previously encountered corporales. |
Relation Discovery with Out-of-Relation Knowledge Base as Supervision (N19-1)
Copied to clipboard
| Challenge: | Existing methods to extract relations from text corpus without annotated data are violated by up to 31%. |
| Approach: | They propose to use out-of-relation knowledge bases to supervise the discovery of unseen relations where relations to discover from the text corpus and those in knowledge bases are not overlapped. |
| Outcome: | The proposed method improves the state-of-the-art relation discovery performance by a large margin. |
Corpora Generation for Grammatical Error Correction (N19-1)
Copied to clipboard
| Challenge: | Grammatical Error Correction (GEC) is a computational task that requires large amounts of data to solve. |
| Approach: | They propose two approaches to generate large parallel datasets for GEC using publicly available Wikipedia edit histories using minimal filtration heuristics and round-trip translation through bridge languages. |
| Outcome: | The proposed methods yield similar sized parallel corpora with around 4B tokens and are far ahead of the state-of-the-art on the CoNLL ‘14 benchmark and the JFLEG task. |
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies (N19-1)
Copied to clipboard
| Challenge: | State-of-the-art LSTM language models learn sequential contingencies with some success . LS models fail to learn other non-local grammatical dependencies, however . |
| Approach: | They compare LSTM language models with RNNGs to examine grammatical dependencies . they find that hierarchical supervision improves learning of non-local dependencies. |
| Outcome: | The proposed model outperforms the existing model on non-local dependencies and learns many of the Island Constraints on the filler-gap dependency. |
Benchmarking Approximate Inference Methods for Neural Structured Prediction (N19-1)
Copied to clipboard
| Challenge: | Structured prediction models often involve complex inference problems for which finding exact solutions is intractable. |
| Approach: | They propose to perform gradient descent with respect to the output structure directly and train a neural network to perform inference. |
| Outcome: | The proposed methods achieve better speed/accuracy/search error trade-off than gradient descent while being faster than exact inference at similar accuracy levels. |
Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent (N19-1)
Copied to clipboard
| Challenge: | Recent research has demonstrated that goal-oriented dialogue agents can achieve striking performance when interacting with human users. |
| Approach: | They develop algorithms to evaluate the robustness of a goal-oriented dialogue agent by carefully designed attacks using adversarial agents. |
| Outcome: | The proposed attacks reduce the advantage of rewards between the attacker and the trained agent from 2.68 to -5.76 on a scale from -10 to 10 for randomized goals. |
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications (N19-1)
Copied to clipboard
| Challenge: | Existing approaches focus on improving accuracy and overlook other aspects such as robustness and interpretability. |
| Approach: | They propose adversarial modifications for link prediction models that identify influential facts and evaluate their sensitivity to addition of fake facts. |
| Outcome: | The proposed model evaluates the robustness of the model to the addition of fake facts and the interpretability of the models. |
Transferable Neural Projection Representations (N19-1)
Copied to clipboard
| Challenge: | Neural word embeddings require lookup and a large memory footprint making it hard to deploy on-device. |
| Approach: | They propose a skip-gram based architecture coupled with Locality-Sensitive Hashing projections to learn efficient dynamically computable representations. |
| Outcome: | The proposed model performs better than previous models on multiple NLP tasks. |
Semantic Role Labeling with Associated Memory Network (N19-1)
Copied to clipboard
| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)
Copied to clipboard
| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
| Approach: | They propose to use auxiliary losses and sentence-level fine-tuning to mitigate greedy decoding issues. |
| Outcome: | The proposed model surpasses the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebank datasets and reduces their parsing time even further. |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
Decomposed Local Models for Coordinate Structure Parsing (N19-1)
Copied to clipboard
| Challenge: | Existing methods for coordination boundary identification are inefficient, even for humans. |
| Approach: | They propose a simple and accurate model for coordination boundary identification . they combine syntactic parsers and neural networks to compute similarity and replaceability features of conjuncts . |
| Outcome: | The proposed model outperforms similarity-based approaches but cannot handle more than two conjuncts in a coordination and multiple coordinations at once. |
Multi-Task Learning for Japanese Predicate Argument Structure Analysis (N19-1)
Copied to clipboard
| Challenge: | Recent work ignores event-nouns or builds a single model for solving both tasks . however, there are interactions between predicates and event-nons, making it difficult to target only predicate. |
| Approach: | They propose a multi-task learning method that targets event-nouns . their results improve performance of both PASA and ENASA tasks . |
| Outcome: | The proposed model improves both PASA and ENASA tasks compared to a single-task model . it is the first work to employ neural networks in ENASA . |
Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
Copied to clipboard
| Challenge: | Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically. |
| Approach: | They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain. |
| Outcome: | The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |
Neural Chinese Address Parsing (N19-1)
Copied to clipboard
| Challenge: | Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems. |
| Approach: | They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure. |
| Outcome: | The proposed model is able to capture complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. |
Learning Hierarchical Discourse-level Structure for Fake News Detection (N19-1)
Copied to clipboard
| Challenge: | Existing methods for capturing discourse-level structure of fake news articles rely on annotated corpora. |
| Approach: | They propose to incorporate hierarchical discourse-level structure of fake and real news articles into detection methods . they propose to learn and construct a discourse- level structure for fake/real news articles . |
| Outcome: | The proposed approach can detect fake news articles based on their contents . it can also identify structure-related properties that can boost fake news understating . |
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion (N19-1)
Copied to clipboard
| Challenge: | Existing datasets for sentence fusion are small and insufficient for training modern neural models. |
| Approach: | They propose a method for automatically-generating fusion examples from raw text . they apply their method to Wikipedia and Sports articles to generate fusion models . |
| Outcome: | The proposed method improves performance on WebSplit when viewed as a sentence fusion task. |
Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation (N19-1)
Copied to clipboard
| Challenge: | Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. |
| Approach: | They propose to rerank a sequence-to-sequence model to improve the informativeness, reasonableness, and grammatically of responses by using externally-trained classifiers targeting each of these factors. |
| Outcome: | The proposed model improves the informativeness, reasonableness, and grammatically of responses. |
Learning to Describe Unknown Phrases with Local and Global Contexts (N19-1)
Copied to clipboard
Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
| Challenge: | Existing methods for contextual guessing and definition generation do not take clues from local contexts. |
| Approach: | They propose a neural description model that takes clues from local and global contexts . they assume that the target phrase is newly emerged and there is no global context . |
| Outcome: | The proposed model takes clues from local and global contexts over existing methods . it is more effective than existing methods for non-standard English explanation . |
Mining Discourse Markers for Unsupervised Sentence Representation Learning (N19-1)
Copied to clipboard
| Challenge: | Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to obtain and are ineffective to extract. |
| Approach: | They propose to automatically discover sentence pairs with relevant discourse markers and apply it to massive amounts of data. |
| Outcome: | The proposed method can learn transferable sentence embeddings from 174 discourse markers even for rare markers such as “coincidentally” or “amazingly”. |
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection (N19-1)
Copied to clipboard
| Challenge: | Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios. |
| Approach: | They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop . |
| Outcome: | The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks. |
Subword-based Compact Reconstruction of Word Embeddings (N19-1)
Copied to clipboard
| Challenge: | Existing word-based word embeddings are based on subword information and memory-shared embeddables. |
| Approach: | They propose a method for reconstructing pre-trained word embeddings using subword information using memory-shared embedds and a variant of the key-value-query self-attention mechanism. |
| Outcome: | The proposed method can imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets. |
Bayesian Learning for Neural Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (N19-1)
Copied to clipboard
| Challenge: | Multi-task learning is an inductive transfer mechanism that leverages information from related tasks to improve the primary model's generalization performance. |
| Approach: | They propose a multitask learning pipeline that finds relevant auxiliary tasks and learns their mixing ratio. |
| Outcome: | The proposed model can find relevant auxiliary tasks and learn their mixing ratio . the proposed model achieves significant performance boosts on several primary tasks . |
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages (N19-1)
Copied to clipboard
| Challenge: | Recent studies have identified both strengths and limitations of recurrent neural networks (RNNs) in applied natural language processing tasks. |
| Approach: | They propose a paradigm that addresses typological differences between languages . they create synthetic versions of English and train them to predict agreement features . |
| Outcome: | The proposed model improves on predicting agreement with subject and object, suggesting that RNNs have a recency bias. |
Attention is not Explanation (N19-1)
Copied to clipboard
| Challenge: | Attention mechanisms have seen wide adoption in neural NLP models. |
| Approach: | They perform extensive experiments to assess the degree to which attention weights provide meaningful "explanations" they find that attention weighted inputs are often uncorrelated with gradient-based measures of feature importance . |
| Outcome: | The proposed model is based on a distribution over attended-to input units . the findings show that attention weights are often uncorrelated with features . |
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
Copied to clipboard
| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |
Information Aggregation for Multi-Head Attention with Routing-by-Agreement (N19-1)
Copied to clipboard
| Challenge: | Existing studies focus on extracting informative or distinct partial-representations from different subspaces, while few studies have paid attention to the aggregation of the extracted partial-Representations. |
| Approach: | They propose to use a routing-by-agreement algorithm to improve multi-head attention by iteratively updating the proportion of how much a part should be assigned to a whole based on agreement between parts and wholes. |
| Outcome: | The proposed algorithm improves the information aggregation for multi-head attention over the standard linear transformation on linguistic probing and machine translation tasks. |
Context Dependent Semantic Parsing over Temporally Structured Data (N19-1)
Copied to clipboard
| Challenge: | Existing semantic parsing tools only allow for natural language interactions, but the graphical interface could be improved significantly. |
| Approach: | They propose a semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface. |
| Outcome: | The proposed architecture outperforms standard sequence generation baselines and achieves sequence-level accuracy of 88.7% on artificial data and 74.8% on real data. |
Structural Scaffolds for Citation Intent Classification in Scientific Publications (N19-1)
Copied to clipboard
| Challenge: | Existing methods for identifying intent of citations are limited by external linguistic resources and hand-engineered features. |
| Approach: | They propose a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. |
| Outcome: | The proposed model achieves a 13.3% increase in F1 score on an existing ACL anthology dataset without external linguistic resources or hand-engineered features as done in existing methods. |
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference (N19-1)
Copied to clipboard
| Challenge: | Existing inference models that rely heavily on unsupervised single-word embeddings struggle to learn implied relationships between pairs of words. |
| Approach: | They propose to use word embeddings to learn and use background knowledge about implied relationships between words that are crucial for cross-sentence inference problems. |
| Outcome: | The proposed models gain 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI, and 8.8% on the adversarial SQu AD datasets. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
Copied to clipboard
| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)
Copied to clipboard
| Challenge: | Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies. |
| Approach: | They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests. |
| Outcome: | The proposed method outperforms baseline models and offers increased interpretability of persuasive speech. |
Recursive Routing Networks: Learning to Compose Modules for Language Understanding (N19-1)
Copied to clipboard
Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen
| Challenge: | Recursive Routing Networks are modular, adaptable models that learn effectively in diverse environments. |
| Approach: | They propose to apply Recursive Routing Networks (RRNs) to natural language understanding by integrating them into existing architectures and recurrent network hidden layers. |
| Outcome: | The proposed model optimizes the parameters of the functions and the meta-learner decision-making component for routing inputs through those functions. |
Structural Neural Encoders for AMR-to-text Generation (N19-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
| Outcome: | The proposed model outperforms tree encoders in the AMR-to-text generation task by 24.40 points. |
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
Copied to clipboard
Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
| Approach: | They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls. |
| Outcome: | The proposed model improves classification performance in English and French separately. |
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (N19-1)
Copied to clipboard
| Challenge: | Existing methods to categorize sentiments and emotions in text are limited. |
| Approach: | They propose to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. |
| Outcome: | The proposed method improves on a recently published dataset for categorizing human needs. |
NLP Whack-A-Mole: Challenges in Cross-Domain Temporal Expression Extraction (N19-1)
Copied to clipboard
| Challenge: | Temporal resolution is an NLP task that is domain-agnostic because of limited lexicons. |
| Approach: | They propose to use a temporal resolution tool built on Newswire text to parse clinical notes in the THYME corpus. |
| Outcome: | The proposed system outperforms current state-of-the-art systems on the THYME corpus with little change in its performance on Newswire texts. |
Document-Level N-ary Relation Extraction with Multiscale Representation Learning (N19-1)
Copied to clipboard
| Challenge: | Existing work on cross-sentence relation extraction is limited to three consecutive sentences, which severely limits recall. |
| Approach: | They propose a multiscale neural architecture for document-level n-ary relation extraction that combines representations learned over various text spans throughout the document and across the subrelation hierarchy. |
| Outcome: | The proposed system outperforms existing methods on biomedical machine reading. |
Inferring Which Medical Treatments Work from Reports of Clinical Trials (N19-1)
Copied to clipboard
| Challenge: | Ideally, one would consult all available evidence from relevant clinical trials. however, these results are primarily disseminated in natural language scientific articles. |
| Approach: | They propose a task that involves inferring results from a full-text article describing randomized controlled trials with respect to a given intervention, comparator, and outcome of interest. |
| Outcome: | The proposed task consists of 10,000+ prompts coupled with full-text articles describing randomized controlled trials. |
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding (N19-1)
Copied to clipboard
| Challenge: | Existing models that use contextual information of dialogues to improve spoken language understanding (SLU) select the wrong history when the histories are similar in content. |
| Approach: | They propose time-aware models that automatically learn the latent time-decay function of the history without a manual time- decay. |
| Outcome: | The proposed models achieve higher F1 scores than state-of-the-art models on a benchmark dataset . |
Dialogue Act Classification with Context-Aware Self-Attention (N19-1)
Copied to clipboard
| Challenge: | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. |
| Approach: | They propose a hierarchical deep neural network to model different levels of utterance and dialogue act semantics and use contextual dependencies to improve performance. |
| Outcome: | The proposed model improves on the Switchboard Dialogue Act Corpus while maintaining high accuracy. |
Affect-Driven Dialog Generation (N19-1)
Copied to clipboard
| Challenge: | Existing systems for end-to-end dialog generation focus on response quality without explicit control over affective content of the responses. |
| Approach: | They propose an affect-driven dialog system which generates emotional responses using a continuous representation of emotions. |
| Outcome: | The proposed system outperforms existing systems in terms of BLEU score and response diversity, and qualitative measures. |
Multi-Level Memory for Task Oriented Dialogs (N19-1)
Copied to clipboard
| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Topic Spotting using Hierarchical Networks with Self Attention (N19-1)
Copied to clipboard
| Challenge: | Existing systems struggle to have consistent long term conversations with the users and fail to build rapport. |
| Approach: | They propose a hierarchical model with self attention for topic spotting . they compare it to previous proposed techniques for topic detection . |
| Outcome: | The proposed model outperforms existing models for topic spotting and deep models for text classification in an online setting. |
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar (N19-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder architectures have shown promise for natural language generation. |
| Approach: | They propose to generate words according to order of first appearance in lexicalized PCFG parse tree . they also combine neural model with symbolic approach to generate syntactic structure . |
| Outcome: | The proposed method improves over sequence-to-sequence baseline in diversity and relevance. |
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
Copied to clipboard
| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |
Continuous Learning for Large-scale Personalized Domain Classification (N19-1)
Copied to clipboard
| Challenge: | Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants. |
| Approach: | They propose a neural-based approach for continuous domain adaption with normalization and regularization to accommodate new domains. |
| Outcome: | The proposed approach outperforms baseline methods on accommodated new domains and existing known domains by a large margin. |
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (N19-1)
Copied to clipboard
| Challenge: | a lack of multilingual training data has hindered development of conversational AI models for task-oriented tasks . a new data set of 57k annotated utterances in english, spanish, and Thai is used to evaluate cross-lingual methods . |
| Approach: | They present a data set of 57k annotated utterances in English, Spanish and Thai . they evaluate three different cross-lingual transfer methods to identify user intents and slots . |
| Outcome: | The proposed model outperforms existing methods in English, Spanish and Thai . the proposed model is based on training data from three languages . |
Evaluating Coherence in Dialogue Systems using Entailment (N19-1)
Copied to clipboard
| Challenge: | Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. |
| Approach: | They propose a set of metrics for evaluating topic coherence using distributed sentence representations and calculable approximations of human judgment using conversational coherency. |
| Outcome: | The proposed metrics can be used as a surrogate for human judgment based on conversational coherence on large-scale datasets and provide an unbiased estimate for the quality of the responses. |
On Knowledge distillation from complex networks for response prediction (N19-1)
Copied to clipboard
| Challenge: | Recent advances in Question Answering have led to the development of very complex models . however, these models are expensive in space and time and require limited resources . |
| Approach: | They propose to use simple models which learn to emulate characteristics of a teacher network . they use a 12GB Tesla K80 GPU to restrict the maximum length of the input document . |
| Outcome: | The proposed model can perform better on a Holl-E dialog dataset. |
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging (N19-1)
Copied to clipboard
| Challenge: | Low-resource language name tagging is an important but challenging task. |
| Approach: | They propose a neural architecture that leverages multi-level adversarial transfer to improve name tagging for low-resource languages. |
| Outcome: | The proposed approach outperforms previous approaches on CoNLL data sets. |
Unsupervised Extraction of Partial Translations for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation systems usually require a large quantity of bilingual parallel data for training. |
| Approach: | They propose an algorithm for extracting from monolingual data what they call partial translations . partial translation is a pair of source and target sentences that contain sequences of tokens that are translations of each other. |
| Outcome: | The proposed algorithm extracts from monolingual data what we call partial translations . it takes only source and target monolingual datasets as input . |
Low-Resource Syntactic Transfer with Unsupervised Source Reordering (N19-1)
Copied to clipboard
| Challenge: | Existing methods for dependency parsing use word order differences between source and target languages. |
| Approach: | They propose a cross-lingual transfer method that takes into account word order differences between source and target languages. |
| Outcome: | The proposed method improves on 68 treebanks (38 languages) on a target language. |
Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown superior performance for non-adversarial methods in more challenging language pairs. |
| Approach: | They propose to use adversarial autoencoder to map monolingual embeddings to a shared space and to put the target encoders as an adversary against the corresponding discriminator. |
| Outcome: | The proposed method is more robust and achieves better performance than previously proposed adversarial and non-adversarial methods. |
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages (N19-1)
Copied to clipboard
| Challenge: | Existing studies show that transfer learning works best when the languages are related. |
| Approach: | They propose to pre-order assisting language sentences to match the word order of the source language and train the parent model. |
| Outcome: | The proposed model can improve translation quality in low-resource scenarios by pre-ordering the assisting language sentences to match the word order of the source language and training the parent model. |
Massively Multilingual Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Multilingual Neural Machine Translation models support translation from multiple source languages into multiple target languages. |
| Approach: | They perform extensive experiments in training massively multilingual NMT models involving up to 103 distinct languages and 204 translation directions simultaneously. |
| Outcome: | The proposed model outperforms the state-of-the-art in low resource settings while supporting up to 59 languages in 116 translation directions. |
A Large-Scale Comparison of Historical Text Normalization Systems (N19-1)
Copied to clipboard
| Challenge: | a large study of historical text normalization is done on eight languages . there is no consensus on the state-of-the-art approach to normalization . |
| Approach: | They present a large study of historical text normalization done on eight languages . they evaluate four different systems based on supervised learning on datasets from eight different languages based in the literature . |
| Outcome: | The proposed methods are based on supervised learning and are available online. |
Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification (N19-1)
Copied to clipboard
| Challenge: | Recent work shows that distributional semantic approaches have difficulty distinguishing between synonyms and antonyms. |
| Approach: | They propose to use monolingual distributional information available in a target language to transfer supervision to other languages using cross-lingual word embeddings. |
| Outcome: | The proposed method improves the transfer of monolingual distributional information to other languages using co-occurrences with discourse markers indicative of antonymy. |
Context-Aware Cross-Lingual Mapping (N19-1)
Copied to clipboard
| Challenge: | Cross-lingual word vectors are typically obtained by fitting an orthogonal matrix that maps the entries of a bilingual dictionary from a source to a target vector space. |
| Approach: | They propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. |
| Outcome: | The proposed approach outperforms context-independent word mapping in translation retrieval. |
Polyglot Contextual Representations Improve Crosslingual Transfer (N19-1)
Copied to clipboard
| Challenge: | Existing methods for crosslingual transfer use multilingual word embeddings, but contextual word representations are not yet available. |
| Approach: | They propose a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. |
| Outcome: | The proposed method compares model models to monolingual and non-contextual variants and shows that polyglot learning can be beneficial for multilingual representations. |
Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)
Copied to clipboard
| Challenge: | Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing . |
| Approach: | They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser. |
| Outcome: | The proposed model can be trained on 40 languages with the help of typological features. |
Recommendations for Datasets for Source Code Summarization (N19-1)
Copied to clipboard
| Challenge: | Code summarization is the task of writing short, natural language descriptions of source code. |
| Approach: | They propose to use a dataset based on 2.1m pairs of Java methods and one sentence method descriptions from over 28k Java projects to write short, natural language code summarizations. |
| Outcome: | The proposed dataset shows that the proposed standards are more effective than previous versions. |
Question Answering as an Automatic Evaluation Metric for News Article Summarization (N19-1)
Copied to clipboard
| Challenge: | Recent work on summarization and headline generation focuses on maximizing ROUGE scores. |
| Approach: | They propose an extrinsic evaluation metric that maximizes ROUGE scores for automatic summarization and headline generation. |
| Outcome: | The proposed model maximizes ROUGE scores while increasing competitive results. |
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
Copied to clipboard
| Challenge: | Neural abstractive summarization systems generate summary texts conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarizing datasets. |
| Approach: | They propose to analyze existing neural abstractive summarization systems by comparing their performance to human-written summaries. |
| Outcome: | The proposed systems perform better than human-written summarizations on different datasets and show that they are able to understand deeper syntactic and semantic structures. |
Jointly Extracting and Compressing Documents with Summary State Representations (N19-1)
Copied to clipboard
| Challenge: | Text summarization is an important NLP problem with a wide range of applications in data-driven industries. |
| Approach: | They propose a neural model that extracts sentences from a document and compresses them. |
| Outcome: | The proposed model generates concise and informa-tive summaries on CNN/DailyMail and Newsroom datasets and human evaluations show it outperforms existing methods. |
News Article Teaser Tweets and How to Generate Them (N19-1)
Copied to clipboard
| Challenge: | A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. |
| Approach: | They propose a benchmark and baseline system for the process of generating teasers. |
| Outcome: | The proposed system is best performing with a pointer network. |
Cross-referencing Using Fine-grained Topic Modeling (N19-1)
Copied to clipboard
| Challenge: | Cross-referencing is a useful study aid for facilitating comprehension of a text, but it requires extensive thematic knowledge and a focused search through the corpus to find such useful connections. |
| Approach: | They propose a system for producing candidate cross-references which can be easily verified by human annotators. |
| Outcome: | a new system can produce cross-references that can be easily verified by human annotators . the system uses fine-grained topic modeling to identify verse pairs which are topically related . |
Conversation Initiation by Diverse News Contents Introduction (N19-1)
Copied to clipboard
| Challenge: | Existing conversation systems assume that the user always initiates conversation and focus on how to respond to the given user’s utterance. |
| Approach: | They propose to generate initial utterance by summarizing and chatting about news articles to avoid boredom by relying on boilerplate utterrances like greetings. |
| Outcome: | The proposed model outperforms baseline models and based on information retrieval based and generation based models. |
Positional Encoding to Control Output Sequence Length (N19-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder models have been successful in natural language generation tasks, but they must be limited to a specified length for abstractive summarization. |
| Approach: | They propose a sinusoidal positional extension to preserve the length constraint so that a neural encoder-decoder model can generate a text of any length even if the target length is unseen in training data. |
| Outcome: | The proposed method can generate a text of any length even if the target length is unseen in training data and improves ROUGE scores. |
The Lower The Simpler: Simplifying Hierarchical Recurrent Models (N19-1)
Copied to clipboard
| Challenge: | Using a simplified version of GRU, we replace the GRUs at the middle layers of hierarchical recurrent models with Fixed-size Ordinally-Forgetting Encoding (FOFE). |
| Approach: | They propose to make the lower layers simpler than the upper ones to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. |
| Outcome: | The proposed models contain less trainable parameters, consume less training time, and achieve slightly better performance than baseline models. |
Using Natural Language Relations between Answer Choices for Machine Comprehension (N19-1)
Copied to clipboard
| Challenge: | Current approaches to the reading comprehension task quantify the relationship between each question and answer choice independently and pick the highest scoring option. |
| Approach: | They propose a method to leverage natural language relations between answer choices to improve machine comprehension. |
| Outcome: | The proposed model improves the performance of a reading comprehension task by leveraging natural language relations between answer choices. |
Saliency Learning: Teaching the Model Where to Pay Attention (N19-1)
Copied to clipboard
| Challenge: | Recent work on explanation and interpretation has introduced methods to provide insights toward the model’s behaviour and predictions, but they do not improve the model's reliability. |
| Approach: | They propose to provide explanation training and ensure alignment of model’s explanation with ground truth explanation to ensure the model makes correct predictions for the right reason. |
| Outcome: | The proposed method produces more reliable predictions while delivering better results compared to traditional models. |
Understanding Dataset Design Choices for Multi-hop Reasoning (N19-1)
Copied to clipboard
| Challenge: | Existing datasets that explicitly focus on multi-hop reasoning are lacking in learning multi-tasking. |
| Approach: | They propose to use sentence-factored models to solve multi-hop question answering tasks . they find spurious correlations in unmasked versions of WikiHop and HotpotQA . |
| Outcome: | The proposed datasets are used to test models on multi-hop question answering tasks. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
Copied to clipboard
| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
Convolutional Self-Attention Networks (N19-1)
Copied to clipboard
| Challenge: | Existing models of self-attention networks lack the ability to capture dependencies regardless of distance and can be enhanced with multi-head attention. |
| Approach: | They propose a convolutional self-attention network which can be enhanced by multi-head attention by allowing the model to attend to information from different representation subspaces. |
| Outcome: | The proposed model outperforms existing models on improving locality of SANs on different language pairs and model settings. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
Copied to clipboard
| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Pre-trained language model representations for language generation (N19-1)
Copied to clipboard
| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
| Approach: | They propose to use pre-trained language model representations to integrate them into sequence to sequence models and apply it to machine translation and abstractive summarization. |
| Outcome: | The proposed model is able to perform 5.3 BLEU in machine translation and 5.3 on the full text version of CNN/DailyMail. |
Pragmatically Informative Text Generation (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to pragmatics have been used to improve the informativeness of generated text in grounded language learning problems. |
| Approach: | They propose to use pragmatics to improve the informativeness of conditional text models . they propose to apply pragmatic reasoning to more traditional language generation tasks . |
| Outcome: | The proposed methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations. |
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (N19-1)
Copied to clipboard
| Challenge: | Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs. |
| Approach: | They propose to use the variational autoencoder (VAE) for probabilistic sentence generation . they propose a variant of WAE that encourages the stochasticity of the encoder . |
| Outcome: | The proposed variant encourages the stochasticity of the encoder while achieving higher BLEU scores. |
Benchmarking Hierarchical Script Knowledge (N19-1)
Copied to clipboard
| Challenge: | Understanding procedural language requires reasoning about hierarchical and temporal relations between events. |
| Approach: | They propose a hierarchical script learning dataset and a cloze task to match video captions with missing procedural details. |
| Outcome: | The proposed model matches video captions with missing procedural details to find out if they can understand the language. |
A large-scale study of the effects of word frequency and predictability in naturalistic reading (N19-1)
Copied to clipboard
| Challenge: | Recent studies have shown separable effects of word frequency and predictability on human sentence processing . other theories hold that apparent effects of frequency are underlyingly effects of predictability . |
| Approach: | They examine the generalizability of this finding to more realistic conditions of sentence processing by studying effects of frequency and predictability in three large-scale naturalistic reading corpora. |
| Outcome: | The results show that word frequency and predictability are significant in isolation but not over and above predictability, and raise doubts about the existence of such a distinction in everyday sentence comprehension. |
Augmenting word2vec with latent Dirichlet allocation within a clinical application (N19-1)
Copied to clipboard
| Challenge: | Existing models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions are not suitable for clinical binary text classification tasks. |
| Approach: | They propose three models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. |
| Outcome: | The proposed models outperform word2vec and LDA models on a clinical binary text classification task. |
On the Idiosyncrasies of the Mandarin Chinese Classifier System (N19-1)
Copied to clipboard
| Challenge: | idiosyncrasies of the Chinese classifier system have been studied, but little work has been done to quantify them with statistical methods. |
| Approach: | They propose an information-theoretic approach to measuring idiosyncrasies in Mandarin Chinese by calculating the mutual information between the distribution over classifiers and distributions over other linguistic quantities. |
| Outcome: | The proposed method reduces uncertainty in Mandarin Chinese classifiers by knowing semantic information about nouns that they modify. |
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging (N19-1)
Copied to clipboard
| Challenge: | Pre-trained neural networks struggle with learning uncommon target-specific patterns. |
| Approach: | They propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining valuable source knowledge. |
| Outcome: | The proposed method achieves state-of-the-art on 3 commonly used datasets. |
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines (N19-1)
Copied to clipboard
| Challenge: | Experimental results show that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages. |
| Approach: | They run experiments on 50 languages covering all morphological language families to compare n-gram models with lstm models. |
| Outcome: | The proposed extension outperforms an lstm language model on 42 languages while its extension which explicitly injects linguistic knowledge outperformed the character-aware neural model on 8 languages. |
Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text (N19-1)
Copied to clipboard
| Challenge: | Using context-sensitive approaches to lemmatization can improve accuracy on unseen and unseense words. |
| Approach: | They propose to use inflection tables and Wikipedia sentences to train a lemmatizer with little or no labeled corpus data to combine type-based learning with context. |
| Outcome: | The proposed model generalizes from unambiguous examples, improving overall and especially on unseen words. |
A Structural Probe for Finding Syntax in Word Representations (N19-1)
Copied to clipboard
| Challenge: | Existing methods for detecting syntactic knowledge do not test whether syntax trees are embedded in a linear transformation of a neural network’s word representation space. |
| Approach: | They propose a structural probe which evaluates whether syntax trees are embedded in a linear transformation of a neural network’s word representation space. |
| Outcome: | The proposed model shows that entire syntax trees are embedded in deep models’ vector geometry. |
CNM: An Interpretable Complex-valued Network for Matching (N19-1)
Copied to clipboard
| Challenge: | Existing work on quantum physics models language understanding using quantum probability . |
| Approach: | They propose a quantum-theoretic framework that unifies different linguistic units in a single complex-valued vector space and a complex-valuable network for semantic matching. |
| Outcome: | The proposed framework achieves comparable performances to strong CNN and RNN baselines on two benchmarking question answering (QA) datasets. |
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (N19-1)
Copied to clipboard
| Challenge: | Recent work on question answering relies on factoid questions with little general knowledge. |
| Approach: | They propose a dataset to capture commonsense question answering with prior knowledge . they extract multiple-choice questions that discriminate between the source and target concepts . |
| Outcome: | The proposed dataset captures commonsense reasoning beyond associations . it obtains 56% accuracy, well below human performance, which is 89% . |
Probing the Need for Visual Context in Multimodal Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Current work on multimodal machine translation (MMT) suggests that the visual modality is either unnecessary or only marginally beneficial. |
| Approach: | They propose to use the visual modality to combine visual and textual information to generate better translations by partially depriving models from source-side textual context. |
| Outcome: | The proposed model can combine visual and textual information to generate better translations under limited textual context. |
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (N19-1)
Copied to clipboard
| Challenge: | Existing language representation models pre-train deep bidirectional representations from unlabeled text without significant task-specific architecture modifications. |
| Approach: | They propose a language representation model that pre-trains bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. |
| Outcome: | The proposed model achieves state-of-the-art results on eleven natural language processing tasks, pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement) |
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (N19-1)
Copied to clipboard
Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
| Challenge: | Existing methods for mitigating bias in machine learning systems rely on access to protected attributes such as race, gender, or age. |
| Approach: | They propose a method for discouraging correlation between predicted probability of an individual’s true occupation and a word embedding of their name. |
| Outcome: | The proposed method reduces race and gender biases, with almost no reduction in the classifier’s overall true positive rate. |