Papers with adaptive segmentation policy

2 papers
Learning Adaptive Segmentation Policy for Simultaneous Translation (2020.emnlp-main)

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Challenge: Experimental results show that adaptive segmentation policies for simultaneous translation are more accurate than current methods . if translation starts before adequate source content is delivered, the quality of translation degrades . waiting for too much source text increases latency, which would hurt accuracy .
Approach: They propose a new adaptive segmentation policy for simultaneous translation based on human interpreters . it learns to segment the source text by considering possible translations produced by the translation model .
Outcome: Experimental results show that the proposed method achieves better accuracy-latency trade-off over state-of-the-art methods.
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation (2022.acl-long)

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Challenge: Existing methods to perform simultaneous speech-to-text translation ignore contextual information and suffer from low translation quality.
Approach: They propose an adaptive segmentation policy for simultaneous speech-to-text translation . it learns to segment the source streaming speech into meaningful units .
Outcome: The proposed method achieves a good accuracy-latency trade-off over state-of-the-art methods on English-German and Chinese-English.

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