An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies

• URL: http://arxiv.org/abs/2103.03233v1
• Date: Thu, 4 Mar 2021 18:55:40 GMT
• Title: An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies
• Authors: Ha Nguyen, Yannick Est\`eve, Laurent Besacier
• Abstract summary: This paper proposes a decoding strategy for end-to-end simultaneous speech translation. We leverage end-to-end models trained in offline mode and conduct an empirical study for two language pairs.
• Score: 17.78024523121448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a decoding strategy for end-to-end simultaneous speech translation. We leverage end-to-end models trained in offline mode and conduct an empirical study for two language pairs (English-to-German and English-to-Portuguese). We also investigate different output token granularities including characters and Byte Pair Encoding (BPE) units. The results show that the proposed decoding approach allows to control BLEU/Average Lagging trade-off along different latency regimes. Our best decoding settings achieve comparable results with a strong cascade model evaluated on the simultaneous translation track of IWSLT 2020 shared task.

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