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In this paper, we present DuTongChuan, a novel context-aware translationmodel for simultaneous interpreting. This model allows to constantly readstreaming text from the Automatic Speech Recognition (ASR) model andsimultaneously determine the boundaries of Information Units (IUs) one afteranother. The detected IU is then translated into a fluent translation with twosimple yet effective decoding strategies: partial decoding and context-awaredecoding. In practice, by controlling the granularity of IUs and the size ofthe context, we can get a good trade-off between latency and translationquality easily. Elaborate evaluation from human translators reveals that oursystem achieves promising translation quality (85.71% for Chinese-English, and86.36% for English-Chinese), specially in the sense of surprisingly gooddiscourse coherence. According to an End-to-End (speech-to-speech simultaneousinterpreting) evaluation, this model presents impressive performance inreducing latency (to less than 3 seconds at most times). Furthermore, wesuccessfully deploy this model in a variety of Baidu's products which havehundreds of millions of users, and we release it as a service in our AIplatform.

DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting