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        <title>Dynamic Sentence Boundary Detection for Simultaneous Translation</title>
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    <name type="personal">
        <namePart type="given">Ruiqing</namePart>
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        <namePart type="given">Chuanqiang</namePart>
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            <title>Proceedings of the First Workshop on Automatic Simultaneous Translation</title>
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            <namePart type="given">Hua</namePart>
            <namePart type="family">Wu</namePart>
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        <name type="personal">
            <namePart type="given">Liang</namePart>
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            <namePart type="given">Zhongjun</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the end-to-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.</abstract>
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    <identifier type="doi">10.18653/v1/2020.autosimtrans-1.1</identifier>
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%0 Conference Proceedings
%T Dynamic Sentence Boundary Detection for Simultaneous Translation
%A Zhang, Ruiqing
%A Zhang, Chuanqiang
%Y Wu, Hua
%Y Cherry, Colin
%Y Huang, Liang
%Y He, Zhongjun
%Y Liberman, Mark
%Y Cross, James
%Y Liu, Yang
%S Proceedings of the First Workshop on Automatic Simultaneous Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F zhang-zhang-2020-dynamic
%X Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the end-to-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.
%R 10.18653/v1/2020.autosimtrans-1.1
%U https://aclanthology.org/2020.autosimtrans-1.1
%U https://doi.org/10.18653/v1/2020.autosimtrans-1.1
%P 1-9

Markdown (Informal)

[Dynamic Sentence Boundary Detection for Simultaneous Translation](https://aclanthology.org/2020.autosimtrans-1.1) (Zhang & Zhang, AutoSimTrans 2020)

• Dynamic Sentence Boundary Detection for Simultaneous Translation (Zhang & Zhang, AutoSimTrans 2020)

ACL

• Ruiqing Zhang and Chuanqiang Zhang. 2020. Dynamic Sentence Boundary Detection for Simultaneous Translation. In Proceedings of the First Workshop on Automatic Simultaneous Translation, pages 1–9, Seattle, Washington. Association for Computational Linguistics.