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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 Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora Gonen, Hila; Jawahar, Ganesh; Seddah, Djamé... In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03161637 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. pp.538-555, ⟨10.18653/v1/2020.acl-main.51⟩ (2020) BASE Show details 2 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 3 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 4 It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT ... Gonen, Hila; Ravfogel, Shauli; Elazar, Yanai. - : arXiv, 2020 BASE Show details 5 The Extraordinary Failure of Complement Coercion Crowdsourcing ... Elazar, Yanai; Basmov, Victoria; Ravfogel, Shauli; Goldberg, Yoav; Tsarfaty, Reut. - : arXiv, 2020 Abstract: Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action -- e.g., "I started a new book I bought last week", where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods. ... : Workshop on Insights from Negative Results in NLP, co-located with EMNLP 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.05971 https://dx.doi.org/10.48550/arxiv.2010.05971 BASE Hide details 6 Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals ... Elazar, Yanai; Ravfogel, Shauli; Jacovi, Alon. - : arXiv, 2020 BASE Show details 7 Break It Down: A Question Understanding Benchmark ... Wolfson, Tomer; Geva, Mor; Gupta, Ankit. - : arXiv, 2020 BASE Show details 8 Unsupervised Domain Clusters in Pretrained Language Models ... Aharoni, Roee; Goldberg, Yoav. - : arXiv, 2020 BASE Show details 9 Nakdan: Professional Hebrew Diacritizer ... Shmidman, Avi; Shmidman, Shaltiel; Koppel, Moshe. - : arXiv, 2020 BASE Show details 10 Interactive Extractive Search over Biomedical Corpora ... Taub-Tabib, Hillel; Shlain, Micah; Sadde, Shoval. - : arXiv, 2020 BASE Show details 11 Unsupervised Distillation of Syntactic Information from Contextualized Word Representations ... Ravfogel, Shauli; Elazar, Yanai; Goldberger, Jacob. - : arXiv, 2020 BASE Show details 12 pyBART: Evidence-based Syntactic Transformations for IE ... Tiktinsky, Aryeh; Goldberg, Yoav; Tsarfaty, Reut. - : arXiv, 2020 BASE Show details 13 Syntactic Search by Example ... Shlain, Micah; Taub-Tabib, Hillel; Sadde, Shoval. - : arXiv, 2020 BASE Show details

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