Papers

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Viewing 721-730 of 926 papers
  • The Curious Case of Neural Text Degeneration

    Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin ChoiICLR2019 Despite considerable advances in neural language modeling, it remains an open question what the best decoding strategy is for text generation from a language model (e.g. to generate a story). The counter-intuitive empirical observation is that even though the…
  • Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

    Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, Jingjing Liu, Jianfeng Gao, Yejin Choi, S. SrinivasaCVPR2019 We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R) Vision-and-Language navigation challenge. Given a natural language…
  • DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

    Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire CardieTACL2019 We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data set…
  • ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

    Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin ChoiAAAI2019 We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as…
  • Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming

    Arindam Mitra, Peter Clark, Oyvind Tafjord, Chitta BaralAAAI2019 While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed…
  • QASC: A Dataset for Question Answering via Sentence Composition

    Tushar Khot, Peter Clark, Michal Guerquin, Paul Edward Jansen, Ashish Sabharwal AAAI2019 Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and…
  • QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

    Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish SabharwalAAAI2019 Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such…
  • On the Capabilities and Limitations of Reasoning for Natural Language Understanding

    Daniel Khashabi, Erfan Sadeqi Azer, Tushar Khot, Ashish Sabharwal, Dan RotharXiv2019 Recent systems for natural language understanding are strong at overcoming linguistic variability for lookup style reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study…
  • Expanding Holographic Embeddings for Knowledge Completion

    Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish SabharwalNeurIPS2018 Neural models operating over structured spaces such as knowledge graphs require a continuous embedding of the discrete elements of this space (such as entities) as well as the relationships between them. Relational embeddings with high expressivity, however…
  • Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

    Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir GlobersonNeurIPS2018 Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A…