Lexically Cohesive Neural Machine Translation with Copy Mechanism

Abstract

Lexically cohesive translations preserve consistency in word choices in document-level translation. We employ a copy mechanism into a context-aware neural machine translation model to allow copying words from previous translation outputs. Different from previous context-aware neural machine translation models that handle all the discourse phenomena implicitly, our model explicitly addresses the lexical cohesion problem by boosting the probabilities to output words consistently. We conduct experiments on Japanese to English translation using an evaluation dataset for discourse translation. The results showed that the proposed model significantly improved lexical cohesion compared to previous context-aware models.

References in corpus (4)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Achieving Human Parity on Automatic Chinese to English News Translation
• Does Neural Machine Translation Benefit from Larger Context?
• Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches