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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 BASE Show details 7 Unsupervised Grammar Induction with Depth-bounded PCFG ... Jin, Lifeng; Doshi-Velez, Finale; Miller, Timothy; Schuler, William; Schwartz, Lane. - : arXiv, 2018 Abstract: There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, gram- mars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models. ... : Accepted by Transactions of the Association for Computational Linguistics ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1802.08545 https://arxiv.org/abs/1802.08545 BASE Hide details

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