https://scholars.lib.ntu.edu.tw/handle/123456789/607154
標題: | Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation | 作者: | Chuang S.-P Chuang Y.-S Chang C.-C Lee H.-Y. HUNG-YI LEE |
關鍵字: | Character recognition;Classification (of information);Computational linguistics;Natural language processing systems;Speech;Text processing;Translation (languages);Auto-regressive;Automatic speech recognition;End to end;Kendall taus;Monotonicity;Performance;Speech translation;Temporal classification;Temporal use;Translation models;Speech recognition | 公開日期: | 2021 | 起(迄)頁: | 1068-1077 | 來源出版物: | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 | 摘要: | We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC), and use CTC-based automatic speech recognition as an auxiliary task to improve the performance. CTC's success on translation is counter-intuitive due to its monotonicity assumption, so we analyze its reordering capability. Kendall's tau distance is introduced as the quantitative metric, and gradient-based visualization provides an intuitive way to take a closer look into the model. Our analysis shows that transformer encoders have the ability to change the word order and points out the future research direction that worth being explored more on non-autoregressive speech translation. ? 2021 Association for Computational Linguistics |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115706890&partnerID=40&md5=4bb52b3372bdaf727b7a6b54e470b403 https://scholars.lib.ntu.edu.tw/handle/123456789/607154 |
顯示於: | 電機工程學系 |
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