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Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau YihEMNLP • 2018 Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions…Rational Recurrences
Hao Peng, Roy Schwartz, Sam Thomson, and Noah A. SmithEMNLP • 2018 Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks…Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
Niket Tandon, Bhavana Dalvi Mishra, Joel Grus, Wen-tau Yih, Antoine Bosselut, Peter ClarkEMNLP • 2018 Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown…SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach
Michael Petrochuk, Luke ZettlemoyerEMNLP • 2018 The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First we show that ambiguity in the…Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources
Gabriel Stanovsky, Mark HopkinsEMNLP • 2018 We propose Odd-Man-Out, a novel task which aims to test different properties of word representations. An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others. We show that this…Structured Alignment Networks for Matching Sentences
Yang Liu, Matt Gardner, Mirella LapataEMNLP • 2018 Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the…SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin ChoiEMNLP • 2018 Given a partial description like"she opened the hood of the car,"humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In this paper, we introduce the task of grounded commonsense inference, unifying…Syntactic Scaffolds for Semantic Structures
Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, and Noah A. SmithEMNLP • 2018 We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We…Understanding Convolutional Neural Networks for Text Classification
Alon Jacovi, Oren Sar Shalom, Yoav GoldbergEMNLP • Workshop: Analyzing and interpreting neural networks for NLP • 2018 We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim…Word Sense Induction with Neural biLM and Symmetric Patterns
Asaf Amrami, Yoav GoldbergEMNLP • 2018 An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. We replace the ngram-based language model (LM) with a recurrent…