Towards Enhancing Faithfulness for Neural Machine Translation

By Rongxiang Weng, Heng Yu, Xiangpeng Wei, Weihua Luo. 2020

25 citations recorded by the source corpus.

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Pascale Fung
  2. Andrea Madotto
  3. Wenliang Dai
  4. Nayeon Lee
  5. Yan Xu
  6. Etsuko Ishii
  7. Ziwei Ji
  8. Dan Su

In Computer Vision and Pattern Recognition

  1. Pascale Fung
  2. Wenliang Dai
  3. Ziwei Ji
  4. Dan Su
  5. Tiezheng Yu
  6. Yejin Bang
  7. Andrea Madotto
  8. Etsuko Ishii

In Natural Language Processing

  1. Pascale Fung
  2. Nayeon Lee
  3. Andrea Madotto
  4. Etsuko Ishii
  5. Yejin Bang
  6. Yan Xu
  7. Rita Frieske
  8. Dan Su

Citations in the corpus