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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 Domain-Adaptive Pretraining Methods for Dialogue Understanding ... 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; Jin, Lifeng; Song, Linfeng. - : Underline Science Inc., 2021 BASE Show details 2 Instance-adaptive training with noise-robust losses against noisy labels ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jin, Lifeng. - : Underline Science Inc., 2021 BASE Show details 3 Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jin, Lifeng; Oh, Byung-Doh. - : Underline Science Inc., 2021 BASE Show details 4 Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories ... Yao, Wenlin; Pan, Xiaoman; Jin, Lifeng. - : arXiv, 2021 BASE Show details 5 Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Jianshu; Jin, Lifeng. - : Underline Science Inc., 2021 BASE Show details 6 The Importance of Category Labels in Grammar Induction with Child-directed Utterances ... Jin, Lifeng; Schuler, William. - : arXiv, 2020 Abstract: Recent progress in grammar induction has shown that grammar induction is possible without explicit assumptions of language-specific knowledge. However, evaluation of induced grammars usually has ignored phrasal labels, an essential part of a grammar. Experiments in this work using a labeled evaluation metric, RH, show that linguistically motivated predictions about grammar sparsity and use of categories can only be revealed through labeled evaluation. Furthermore, depth-bounding as an implementation of human memory constraints in grammar inducers is still effective with labeled evaluation on multilingual transcribed child-directed utterances. ... : The 16th International Conference on Parsing Technologies (IWPT 2020) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2006.11646 https://dx.doi.org/10.48550/arxiv.2006.11646 BASE Hide details 7 Unsupervised Grammar Induction with Depth-bounded PCFG ... Jin, Lifeng; Doshi-Velez, Finale; Miller, Timothy. - : arXiv, 2018 BASE Show details

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