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Viewing 18 papers from 2018 in AllenNLP
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    • EMNLP 2018
      Yang Liu, Matt Gardner, Mirella Lapata
      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 inherent structure of the sentence. When…  (More)
    • EMNLP 2018
      Gabriel Stanovsky, Mark Hopkins
      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 simple setup is capable of teasing out…  (More)
    • EMNLP 2018
      Niket Tandon, Bhavana Dalvi Mishra, Joel Grus, Wen-tau Yih, Antoine Bosselut, Peter Clark
      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 impressive progress in this task, their…  (More)
    • EMNLP 2018
      Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi
      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 natural language inference and commonsense…  (More)
    • EMNLP 2018
      Hao Peng, Roy Schwartz, Sam Thomson, and Noah A. Smith
      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 (CNNs) and weighted finite state…  (More)
    • EMNLP 2018
      Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, and Noah A. Smith
      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 improve over strong baselines on…  (More)
    • EMNLP 2018
      Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime Carbonell
      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 languages make it a challenging problem…  (More)
    • EMNLP 2018
      Matthew Peters, Mark Neumann, Wen-tau Yih, and Luke Zettlemoyer
      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 these models are so effective. In this…  (More)
    • ACL 2018
      Vidur Joshi, Matthew Peters, and Mark Hopkins
      We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Jour- nal…  (More)
    • ACL • NLP OSS Workshop 2018
      Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer
      This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation…  (More)
    • ACL 2018
      Maarten Sap, Hannah Rashkin, Emily Allaway, Noah A. Smith and Yejin Choi
      We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we…  (More)
    • ACL 2018
      Eunsol Choi, Omer Levy, Yejin Choi and Luke Zettlemoyer
      We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at…  (More)
    • Award Best Paper Award
      ACL • RepL4NLP Workshop 2018
      Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. Smith
      While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a nonlinguistic task: recalling elements from its…  (More)
    • ACL 2018
      Christopher Clark and Matt Gardner
      We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from…  (More)
    • Award Best Paper Award
      NAACL 2018
      Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
      We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states…  (More)
    • NAACL 2018
      Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Sam Bowman and Noah A. Smith
      Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such…  (More)
    • NAACL-HLT 2018 Dataset
      Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
      Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research pur- poses (PeerRead v1), providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the…  (More)
    • ACL 2018
      Roy Schwartz, Sam Thomson and Noah A. Smith
      Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with…  (More)