Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to train simultaneous machine translation agents have been used to find the optimal action sequences for translation quality and lag. |
| Approach: | They propose a supervised learning approach that detects minimum reads required for generating target tokens by comparing simultaneous translations against full-sentence translations. |
| Outcome: | The proposed method produces much higher quality translations while minimizing the average lag in simultaneous translation. |
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