Papers

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Viewing 901-910 of 1033 papers
  • Learning Interpretable Spatial Operations in a Rich 3D Blocks World

    Yonatan Bisk, Kevin J. Shih, Yejin Choi, and Daniel MarcuAAAI2018 In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex…
  • Question Answering as Global Reasoning over Semantic Abstractions

    Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan RothAAAI2018 We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To…
  • SciTail: A Textual Entailment Dataset from Science Question Answering

    Tushar Khot, Ashish Sabharwal, and Peter ClarkAAAI2018 We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created solely from natural sentences that already exist independently…
  • Commonsense Knowledge in Machine Intelligence

    Niket Tandon, Aparna S. Varde, Gerard de MeloSIGMOD Record2017 There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual details, common sense for human-like reasoning and natural language…
  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, Kai-Wei ChangEMNLP2017 Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and…
  • Dynamic Entity Representations in Neural Language Models

    Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, Noah A. SmithEMNLP2017 Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their…
  • Zero-Shot Activity Recognition with Verb Attribute Induction

    Rowan Zellers, Yejin ChoiEMNLP2017 In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such as being a light movement, a social act, and short in…
  • Answering Complex Questions Using Open Information Extraction

    Tushar Khot, Ashish Sabharwal, and Peter ClarkACL2017 While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to…
  • Learning a Neural Semantic Parser from User Feedback

    Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke ZettlemoyerACL2017 We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to…
  • Semi-supervised sequence tagging with bidirectional language models

    Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell PowerACL2017 Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive…