DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting
This addresses the problem of real-time, low-latency translation for applications like speech-to-speech interpreting, with deployment in products serving hundreds of millions of users, though it appears incremental as it builds on existing ASR and translation methods.
The paper tackles simultaneous interpreting by introducing DuTongChuan, a context-aware translation model that balances latency and translation quality, achieving over 85% translation accuracy and reducing latency to under 3 seconds in evaluations.
In this paper, we present DuTongChuan, a novel context-aware translation model for simultaneous interpreting. This model allows to constantly read streaming text from the Automatic Speech Recognition (ASR) model and simultaneously determine the boundaries of Information Units (IUs) one after another. The detected IU is then translated into a fluent translation with two simple yet effective decoding strategies: partial decoding and context-aware decoding. In practice, by controlling the granularity of IUs and the size of the context, we can get a good trade-off between latency and translation quality easily. Elaborate evaluation from human translators reveals that our system achieves promising translation quality (85.71% for Chinese-English, and 86.36% for English-Chinese), specially in the sense of surprisingly good discourse coherence. According to an End-to-End (speech-to-speech simultaneous interpreting) evaluation, this model presents impressive performance in reducing latency (to less than 3 seconds at most times). Furthermore, we successfully deploy this model in a variety of Baidu's products which have hundreds of millions of users, and we release it as a service in our AI platform.