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Can LSTM Learn to Capture Agreement? The Case of Basque
Shauli Ravfogel, Francis M. Tyers, Yoav GoldbergEMNLP • Workshop: Analyzing and interpreting neural networks for NLP • 2018 Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human…Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing
Jonathan Herzig, Jonathan BerantEMNLP • 2018 Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot…Dissecting Contextual Word Embeddings: Architecture and Representation
Matthew Peters, Mark Neumann, Wen-tau Yih, and Luke ZettlemoyerEMNLP • 2018 Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why…Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime CarbonellEMNLP • 2018 For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across…Neural Metaphor Detection in Context
Ge Gao, Eunsol Choi, Yejin Choi and Luke ZettlemoyerEMNLP • 2018 We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of…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…