# Facilitating Terminology Translation with Target Lemma Annotations

arXiv: https://arxiv.org/abs/2101.10035 · HTML: https://mlsanity.com/paper/2101.10035/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [Lexically Constrained Neural Machine Translation with Levenshtein Transformer](https://mlsanity.com/paper/2004.12681/) (2020)
- [Linguistic Input Features Improve Neural Machine Translation](https://mlsanity.com/paper/1606.02892/) (2016)
- [Neural Machine Translation Decoding with Terminology Constraints](https://mlsanity.com/paper/1805.03750/) (2018)

## Built on by (most cited first, 20 of 20)

- [Democratizing Neural Machine Translation with OPUS-MT](https://mlsanity.com/paper/2212.01936/) (2022)
- [On the Evaluation of Machine Translation for Terminology Consistency](https://mlsanity.com/paper/2106.11891/) (2021)
- [Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation](https://mlsanity.com/paper/2109.07439/) (2021)
- [Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting](https://mlsanity.com/paper/2310.05824/) (2023)
- [End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages](https://mlsanity.com/paper/2106.12398/) (2021)
- [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://mlsanity.com/paper/2109.04620/) (2021)
- [Dynamic Terminology Integration for COVID-19 and other Emerging Domains](https://mlsanity.com/paper/2109.04708/) (2021)
- [Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints](https://mlsanity.com/paper/2306.12089/) (2023)
- [SOTASTREAM: A Streaming Approach to Machine Translation Training](https://mlsanity.com/paper/2308.07489/) (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)
- [Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs](https://mlsanity.com/paper/2206.08709/) (2022)
- [Negative Lexical Constraints in Neural Machine Translation](https://mlsanity.com/paper/2308.03601/) (2023)
- [From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2412.00966/) (2024)
- [A Template-based Method for Constrained Neural Machine Translation](https://mlsanity.com/paper/2205.11255/) (2022)
- [Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases](https://mlsanity.com/paper/2207.06729/) (2022)
- [From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees](https://mlsanity.com/paper/2209.14142/) (2022)
- [Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System](https://mlsanity.com/paper/2508.18701/) (2025)
- [The Constitution Speaks Nheengatu: An Open MT System and Reproducible Corpus Pipeline for the Amazonian L'ingua Geral](https://mlsanity.com/paper/2610.14760/) (2026)

## Abstract (arXiv)

Most of the recent work on terminology integration in machine translation has assumed that terminology translations are given already inflected in forms that are suitable for the target language sentence. In day-to-day work of professional translators, however, it is seldom the case as translators work with bilingual glossaries where terms are given in their dictionary forms; finding the right target language form is part of the translation process. We argue that the requirement for apriori specified target language forms is unrealistic and impedes the practical applicability of previous work. In this work, we propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. We show that systems trained on such augmented data are readily usable for terminology integration in real-life translation scenarios. Our experiments on terminology translation into the morphologically complex Baltic and Uralic languages show an improvement of up to 7 BLEU points over baseline systems with no means for terminology integration and an average improvement of 4 BLEU points over the previous work. Results of the human evaluation indicate a 47.7% absolute improvement over the previous work in term translation accuracy when translating into Latvian.