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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 – 7 of 7 1 The statistical advantage of automatic NLG metrics at the system level ... 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; Jia, Robin; Wei, Johnny. - : Underline Science Inc., 2021 BASE Show details 2 Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? ... 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; ., Jordan; Barrow, Joe. - : Underline Science Inc., 2021 BASE Show details 3 Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bartolo, Max; Jia, Robin. - : Underline Science Inc., 2021 BASE Show details 4 The statistical advantage of automatic NLG metrics at the system level ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jia, Robin; Wei, Johnny. - : Underline Science Inc., 2021 BASE Show details 5 Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality ... Lee, Mina; Donahue, Chris; Jia, Robin; Iyabor, Alexander; Liang, Percy. - : arXiv, 2021 Abstract: We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context. To assist humans with writing, lexical substitution systems can suggest words that humans cannot easily think of. However, existing benchmarks depend on human recall as the only source of data, and therefore lack coverage of the substitutes that would be most helpful to humans. Furthermore, annotators often provide substitutes of low quality, which are not actually appropriate in the given context. We collect higher-coverage and higher-quality data by framing lexical substitution as a classification problem, guided by the intuition that it is easier for humans to judge the appropriateness of candidate substitutes than conjure them from memory. To this end, we use a context-free thesaurus to produce candidates and rely on human judgement to determine contextual appropriateness. Compared to the previous largest benchmark, our Swords benchmark has 4.1x more substitutes per target word ... : Published as a conference paper at NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2106.04102 https://arxiv.org/abs/2106.04102 BASE Hide details 6 Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality ... NAACL 2021 2021; Donahue, Chris; Iyabor, Alexander. - : Underline Science Inc., 2021 BASE Show details 7 Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Dieuwke; Jia, Robin. - : Underline Science Inc., 2021 BASE Show details

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