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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 – 8 of 8 1 An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker ... Lin, Yen-Ting; Chen, Yun-Nung. - : arXiv, 2021 BASE Show details 2 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... Chuang, Yung-Sung; Gao, Mingye; Luo, Hongyin. - : arXiv, 2021 BASE Show details 3 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... 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; Chen, Yun-Nung; Chuang, Yung-Sung; Gao, Mingye; Glass, James; Lee, Hung-yi; Li, Shang-Wen; Luo, Hongyin. - : Underline Science Inc., 2021 Abstract: Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/q9fa-ew67 https://underline.io/lecture/30157-mitigating-biases-in-toxic-language-detection-through-invariant-rationalization BASE Hide details 4 CLUSE: Cross-Lingual Unsupervised Sense Embeddings ... Chi, Ta-Chung; Chen, Yun-Nung. - : arXiv, 2018 BASE Show details 5 Natural Language Generation by Hierarchical Decoding with Linguistic Patterns ... Su, Shang-Yu; Lo, Kai-Ling; Yeh, Yi-Ting. - : arXiv, 2018 BASE Show details 6 Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation ... Su, Shang-Yu; Chen, Yun-Nung. - : arXiv, 2018 BASE Show details 7 BCWS: Bilingual Contextual Word Similarity ... Chi, Ta-Chung; Shih, Ching-Yen; Chen, Yun-Nung. - : arXiv, 2018 BASE Show details 8 Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks ... Chen, Yun-Nung; Hakkani-Tur, Dilek; Tur, Gokhan. - : arXiv, 2016 BASE Show details

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