# Modeling Fluency and Faithfulness for Diverse Neural Machine Translation

arXiv: https://arxiv.org/abs/1912.00178 · HTML: https://mlsanity.com/paper/1912.00178/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://mlsanity.com/paper/2307.03987/) (2023)
- [Prevent the Language Model from being Overconfident in Neural Machine Translation](https://mlsanity.com/paper/2105.11098/) (2021)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation](https://mlsanity.com/paper/2205.14333/) (2022)
- [Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework](https://mlsanity.com/paper/2203.09053/) (2022)
- [Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation](https://mlsanity.com/paper/2406.05494/) (2024)
- [Frequency-Aware Contrastive Learning for Neural Machine Translation](https://mlsanity.com/paper/2112.14484/) (2021)
- [Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation](https://mlsanity.com/paper/2106.06751/) (2021)
- [Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset](https://mlsanity.com/paper/2503.23899/) (2025)
- [Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation](https://mlsanity.com/paper/2401.04972/) (2024)
- [How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension](https://mlsanity.com/paper/2504.12314/) (2025)
- [ChatGPT Incorrectness Detection in Software Reviews](https://mlsanity.com/paper/2403.16347/) (2024)

## Abstract (arXiv)

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.