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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 The Effect of Chinese Proficiency on Determining Temporal Adverb Position by Native Japanese Speakers Learning Chinese Tamaoka, Katsuo; Zhang, Jingyi In: Front Psychol (2022) BASE Show details 2 Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation ... Yang, Gengcong; Zhang, Jingyi; Zhang, Yong. - : arXiv, 2021 BASE Show details 3 A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ... 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; van Genabith, Josef; Zhang, Jingyi. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.24 Abstract: Word alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine translation in many different ways, such as missing word detection, annotation transfer and lexicon injection. Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) and recently neural word alignment models. This paper presents a bidirectional Transformer based alignment (BTBA) model for unsupervised learning of the word alignment task. Our BTBA model predicts the current target word by attending the source context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA model to ... 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/pmd0-7v13 https://underline.io/lecture/25375-a-bidirectional-transformer-based-alignment-model-for-unsupervised-word-alignment BASE Hide details 4 Reliability and validity of the Chinese version of Oldenburg Burnout Inventory for Chinese nurses Xu, Huiwen; Yuan, Yuan; Gong, Weijuan... In: Nurs Open (2021) BASE Show details 5 UniMa at SemEval-2018 Task 7 : semantic relation extraction and classification from scientific publications Ponzetto, Simone Paolo; Zhang, Jingyi; Keiper, Thorsten. - : Association for Computational Linguistics, 2018 BASE Show details 6 Queer Bodies, Queer Lives in China English Contact Literature Paiz Joshua M.; Comeau Anthony; Zhu Junhan... In: Open Linguistics, Vol 4, Iss 1, Pp 147-162 (2018) (2018) BASE Show details 7 Introduction of the World Organic Language Teaching Approach Zhang, Jingyi In: Chinese Language Teaching Methodology and Technology (2017) BASE Show details 8 Parents’ Guidelines for the Transition from Chinese Homes to American Preschools Zhang, Jingyi In: Early and Special Education Graduate Projects and Theses (2009) BASE Show details

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