Modeling Bilingual Conversational Characteristics for Neural Chat Translation

@article{Liang2021ModelingBC,
  title={Modeling Bilingual Conversational Characteristics for Neural Chat Translation},
  author={Yunlong Liang and Fandong Meng and Yufeng Chen and Jinan Xu and Jie Zhou},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11164}
}
  • Yunlong Liang, Fandong Meng, +2 authors Jie Zhou
  • Published 2021
  • Computer Science
  • ArXiv
Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of… Expand
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
  • Yunlong Liang, Chulun Zhou, +4 authors Jie Zhou
  • Computer Science
  • ArXiv
  • 2021
TLDR
This paper proposes to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model, and designs four auxiliary tasks including monolingual response generation, cross-lingual responsegeneration, next utterance discrimination, and speaker identification. Expand

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