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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 13 of 13 1 COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics ... Qin, Lianhui; Welleck, Sean; Khashabi, Daniel. - : arXiv, 2022 BASE Show details 2 Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ... Zhao, Jieyu; Khashabi, Daniel; Khot, Tushar. - : arXiv, 2021 BASE Show details 3 Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies ... Geva, Mor; Khashabi, Daniel; Segal, Elad. - : arXiv, 2021 BASE Show details 4 Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chang, Kai-Wei; Khashabi, Daniel. - : Underline Science Inc., 2021 BASE Show details 5 NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics ... Lu, Ximing; Welleck, Sean; West, Peter. - : arXiv, 2021 BASE Show details 6 ParsiNLU: A Suite of Language Understanding Challenges for Persian ... Khashabi, Daniel; Cohan, Arman; Shakeri, Siamak. - : arXiv, 2020 BASE Show details 7 Evaluating Models' Local Decision Boundaries via Contrast Sets ... Gardner, Matt; Artzi, Yoav; Basmova, Victoria. - : arXiv, 2020 BASE Show details 8 TransOMCS: From Linguistic Graphs to Commonsense Knowledge ... Zhang, Hongming; Khashabi, Daniel; Song, Yangqiu; Roth, Dan. - : arXiv, 2020 Abstract: Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations, which are not feasible on a large scale. In this paper, we explore a practical way of mining commonsense knowledge from linguistic graphs, with the goal of transferring cheap knowledge obtained with linguistic patterns into expensive commonsense knowledge. The result is a conversion of ASER [Zhang et al., 2020], a large-scale selectional preference knowledge resource, into TransOMCS, of the same representation as ConceptNet [Liu and Singh, 2004] but two orders of magnitude larger. Experimental results demonstrate the transferability of linguistic knowledge to commonsense knowledge and the effectiveness of the proposed approach in terms of quantity, novelty, and quality. TransOMCS is publicly available at: https://github.com/HKUST-KnowComp/TransOMCS. ... : Accepted by IJCAI 2020 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2005.00206 https://arxiv.org/abs/2005.00206 BASE Hide details 9 Solving Hard Coreference Problems ... Peng, Haoruo; Khashabi, Daniel; Roth, Dan. - : arXiv, 2019 BASE Show details 10 On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections ... Khashabi, Daniel; Azer, Erfan Sadeqi; Khot, Tushar. - : arXiv, 2019 BASE Show details 11 Question Answering as Global Reasoning over Semantic Abstractions ... Khashabi, Daniel; Khot, Tushar; Sabharwal, Ashish. - : arXiv, 2019 BASE Show details 12 Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims ... Chen, Sihao; Khashabi, Daniel; Yin, Wenpeng. - : arXiv, 2019 BASE Show details 13 Distributed knowledge based clinical auto-coding system Kaur, Rajvir (S33301). - : U.S., Association for Computational Linguistics, 2019 BASE Show details

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