Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
By Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, Dong Yu. 2019
28 citations recorded by the source corpus.
Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution. However, the strategy casts all the portion of the distribution to the ground truth word and ignores other words in the target vocabulary even when the ground truth word cannot dominate the distribution. To address the problem of teacher forcing, we propose a method to introduce an evaluation module to guide the distribution of the prediction. The evaluation module accesses each prediction from the perspectives of fluency and faithfulness to encourage the model to generate the word which has a fluent connection with its past and future translation and meanwhile tends to form a translation equivalent in meaning to the source. The experiments on multiple translation tasks show that our method can achieve significant improvements over strong baselines.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
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Citations in the corpus
- Survey of Hallucination in Natural Language Generation (2022, background): ““Faithfulness” refers to the amount of source meaning that is faithfully expressed in the translation, and it is used interchangeably with the term “adequacy” [49, 186].”
- Towards Enhancing Faithfulness for Neural Machine Translation (2020, background): “Then, several related researches about improve faithfulness of NMT (Kong et al., 2019; Zheng et al., 2019; Chen et al., 2019; Feng et al., 2020) or exploiting translations for improving NMT (Xia et al., 2017; Wang et al., 2019) also be reported (line 7-12).”