# Context Gates for Neural Machine Translation

arXiv: https://arxiv.org/abs/1608.06043 · HTML: https://mlsanity.com/paper/1608.06043/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 10 of 10)

- [G-Transformer for Document-level Machine Translation](https://mlsanity.com/paper/2105.14761/) (2021)
- [Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation](https://mlsanity.com/paper/2203.08442/) (2022)
- [Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models](https://mlsanity.com/paper/2103.17151/) (2021)
- [Revisiting Negation in Neural Machine Translation](https://mlsanity.com/paper/2107.12203/) (2021)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models](https://mlsanity.com/paper/2412.11187/) (2024)
- [Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer](https://mlsanity.com/paper/2311.12856/) (2023)
- [You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models](https://mlsanity.com/paper/2509.14031/) (2025)
- [Sequence Shortening for Context-Aware Machine Translation](https://mlsanity.com/paper/2402.01416/) (2024)
- [Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation](https://mlsanity.com/paper/2609.28117/) (2026)

## Abstract (arXiv)

In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency. Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. To address this problem, we propose context gates which dynamically control the ratios at which source and target contexts contribute to the generation of target words. In this way, we can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts. Experiments show that our approach significantly improves upon a standard attention-based NMT system by +2.3 BLEU points.