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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. - : Underline Science Inc., 2021 BASE Show 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; Chen, Yun-Nung. - : arXiv, 2018 Abstract: Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion. However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge. This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size. ... : Published in NAACL-HLT 2018, the first two authors have equal contributions ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1808.02747 https://dx.doi.org/10.48550/arxiv.1808.02747 BASE Hide 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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