# Learning from Chunk-based Feedback in Neural Machine Translation

arXiv: https://arxiv.org/abs/1806.07169 · HTML: https://mlsanity.com/paper/1806.07169/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings](https://mlsanity.com/paper/2411.05986/) (2024)
- [MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling](https://mlsanity.com/paper/2503.12123/) (2025)
- [Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training](https://mlsanity.com/paper/2307.08416/) (2023)
- [Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation](https://mlsanity.com/paper/2406.02267/) (2024)
- [Error-Aware Interactive Semantic Parsing of OpenStreetMap](https://mlsanity.com/paper/2106.11739/) (2021)

## Abstract (arXiv)

We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation. We propose a simple and effective way of utilizing such feedback in NMT training. We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback. We conduct a series of simulation experiments to test the effectiveness of the proposed method. Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61% BLEU absolute.