Shun-Po Chuang


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Anticipation-Free Training for Simultaneous Machine Translation
Chih-Chiang Chang | Shun-Po Chuang | Hung-yi Lee
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

Simultaneous machine translation (SimulMT) speeds up the translation process by starting to translate before the source sentence is completely available. It is difficult due to limited context and word order difference between languages. Existing methods increase latency or introduce adaptive read-write policies for SimulMT models to handle local reordering and improve translation quality. However, the long-distance reordering would make the SimulMT models learn translation mistakenly. Specifically, the model may be forced to predict target tokens when the corresponding source tokens have not been read. This leads to aggressive anticipation during inference, resulting in the hallucination phenomenon. To mitigate this problem, we propose a new framework that decompose the translation process into the monotonic translation step and the reordering step, and we model the latter by the auxiliary sorting network (ASN). The ASN rearranges the hidden states to match the order in the target language, so that the SimulMT model could learn to translate more reasonably. The entire model is optimized end-to-end and does not rely on external aligners or data. During inference, ASN is removed to achieve streaming. Experiments show the proposed framework could outperform previous methods with less latency.


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Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
Shun-Po Chuang | Yung-Sung Chuang | Chih-Chiang Chang | Hung-yi Lee
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Shun-Po Chuang | Tzu-Wei Sung | Alexander H. Liu | Hung-yi Lee
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Speech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves the ST performance, in which the recognition decoder generates the text of the source language, and the translation decoder obtains the final translations based on the output of the recognition decoder. Because whether the output of the recognition decoder has the correct semantics is more critical than its accuracy, we propose to improve the multitask ST model by utilizing word embedding as the intermediate.