ETRI Journal

  • 제43권5호
  • /
  • Pages.775-786
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  • 2021
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Simultaneous neural machine translation with a reinforced attention mechanism

  • Lee, YoHan (Language Intelligence Research Section, Electronics and Telecommunications Research Institutes) ;
  • Shin, JongHun (Language Intelligence Research Section, Electronics and Telecommunications Research Institutes) ;
  • Kim, YoungKil (Language Intelligence Research Section, Electronics and Telecommunications Research Institutes)
  • 투고 : 2020.09.17
  • 심사 : 2021.02.25
  • 발행 : 2021.10.01
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초록

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

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과제정보

This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (R7119-16-1001, core technology development of the real-time simultaneous speech translation based on knowledge enhancement).

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