# Neural Machine Translation Decoding with Terminology Constraints

arXiv: https://arxiv.org/abs/1805.03750 · HTML: https://mlsanity.com/paper/1805.03750/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 30 of 36)

- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://mlsanity.com/paper/2112.08726/) (2021)
- [COLLIE: Systematic Construction of Constrained Text Generation Tasks](https://mlsanity.com/paper/2307.08689/) (2023)
- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Controlling Conditional Language Models without Catastrophic Forgetting](https://mlsanity.com/paper/2112.00791/) (2021)
- [General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining](https://mlsanity.com/paper/2203.10929/) (2022)
- [Facilitating Terminology Translation with Target Lemma Annotations](https://mlsanity.com/paper/2101.10035/) (2021)
- [Domain Terminology Integration into Machine Translation: Leveraging Large Language Models](https://mlsanity.com/paper/2310.14451/) (2023)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation](https://mlsanity.com/paper/2109.07439/) (2021)
- [Encouraging Neural Machine Translation to Satisfy Terminology Constraints](https://mlsanity.com/paper/2106.03730/) (2021)
- [Integrating Vectorized Lexical Constraints for Neural Machine Translation](https://mlsanity.com/paper/2203.12210/) (2022)
- [WeTS: A Benchmark for Translation Suggestion](https://mlsanity.com/paper/2110.05151/) (2021)
- [Multilingual Sentence Transformer as A Multilingual Word Aligner](https://mlsanity.com/paper/2301.12140/) (2023)
- [Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting](https://mlsanity.com/paper/2310.05824/) (2023)
- [GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation](https://mlsanity.com/paper/2105.14913/) (2021)
- [Controlling Text Edition by Changing Answers of Specific Questions](https://mlsanity.com/paper/2105.11018/) (2021)
- [Constraining Linear-chain CRFs to Regular Languages](https://mlsanity.com/paper/2106.07306/) (2021)
- [End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages](https://mlsanity.com/paper/2106.12398/) (2021)
- [Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction](https://mlsanity.com/paper/2105.05498/) (2021)
- [Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints](https://mlsanity.com/paper/2306.12089/) (2023)
- [Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach](https://mlsanity.com/paper/2409.09009/) (2024)
- [Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models](https://mlsanity.com/paper/2507.18263/) (2025)
- [CUNI systems for WMT21: Terminology translation Shared Task](https://mlsanity.com/paper/2109.09350/) (2021)
- [Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding](https://mlsanity.com/paper/2204.00871/) (2022)
- [Disambiguated Lexically Constrained Neural Machine Translation](https://mlsanity.com/paper/2305.17351/) (2023)
- [Negative Lexical Constraints in Neural Machine Translation](https://mlsanity.com/paper/2308.03601/) (2023)
- [On Translating Technical Terminology: A Translation Workflow for Machine-Translated Acronyms](https://mlsanity.com/paper/2409.17943/) (2024)
- [Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation](https://mlsanity.com/paper/2410.00683/) (2024)
- [Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)](https://mlsanity.com/paper/2412.18367/) (2024)

## Abstract (arXiv)

Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.