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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 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.457/ Abstract: In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process. Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparameter to control the strength of noise resistance. This work proposes novel instance-adaptive training frameworks to change dataset-wise hyperparameters of noise resistance in such losses to be instance-specific. Such instance-specific noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main models. Experiments on noisy and corrupted NLP datasets ... Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37479-instance-adaptive-training-with-noise-robust-losses-against-noisy-labels https://dx.doi.org/10.48448/59p6-ak38 BASE Hide 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. - : arXiv, 2018 BASE Show details

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