mlsanityData to 13 Oct 2026

On the Evaluation of Machine Translation for Terminology Consistency

arXiv 2106.11891 ↗First posted 22 Jun 2021cs.CLcited by 15

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Abstract (arXiv)

As neural machine translation (NMT) systems become an important part of professional translator pipelines, a growing body of work focuses on combining NMT with terminologies. In many scenarios and particularly in cases of domain adaptation, one expects the MT output to adhere to the constraints provided by a terminology. In this work, we propose metrics to measure the consistency of MT output with regards to a domain terminology. We perform studies on the COVID-19 domain over 5 languages, also performing terminology-targeted human evaluation. We open-source the code for computing all proposed metrics: https://github.com/mahfuzibnalam/terminology_evaluation

Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

12 of 20 references.

References · 12

  1. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 720
  2. 2017Lexically Constrained Decoding for Sequence Generation Using Grid Beam Searchcited by 234
  3. 2018Scaling Neural Machine Translationcited by 204
  4. 2018Marian: Fast Neural Machine Translation in C++cited by 203
  5. 2018Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translationcited by 181
  6. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  7. 2016Guided Open Vocabulary Image Captioning with Constrained Beam Searchcited by 105
  8. 2019Training Neural Machine Translation To Apply Terminology Constraintscited by 73
  9. 2020TICO-19: the Translation Initiative for Covid-19cited by 67
  10. 2016Incorporating Discrete Translation Lexicons into Neural Machine Translationcited by 33
  11. 2021Facilitating Terminology Translation with Target Lemma Annotationscited by 19
  12. 2020A Multilingual Neural Machine Translation Model for Biomedical Datacited by 7

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 6; 2024: 2; 2025: 0; 2026: 2.

15 papers.

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  1. 2023Benchmarking Machine Translation with Cultural Awarenesscited by 53
  2. 2023BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translationcited by 9
  3. 2023Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpuscited by 7
  4. 2022A Bilingual Parallel Corpus with Discourse Annotationscited by 4
  5. 2021Dynamic Terminology Integration for COVID-19 and other Emerging Domainscited by 3
  6. 2021CUNI systems for WMT21: Terminology translation Shared Taskcited by 2
  7. 2021Lingua Custodia's participation at the WMT 2021 Machine Translation using Terminologies shared taskcited by 2
  8. 2024KpopMT: Translation Dataset with Terminology for Kpop Fandomcited by 2
  9. 2023Disambiguated Lexically Constrained Neural Machine Translationcited by 1
  10. 2024On Translating Technical Terminology: A Translation Workflow for Machine-Translated Acronymscited by 1
  11. 2026Improving Term Evaluation in Machine Translation: Variation Matterscited by 1
  12. 2022A Template-based Method for Constrained Neural Machine Translationcited by 0
3 more
  1. 2023Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translationcited by 0
  2. 2023Improving Neural Machine Translation by Multi-Knowledge Integration with Promptingcited by 0
  3. 2026TermJudge: A Document-Level Metric Judging, Not Counting, Terminology in Machine Translation Evaluationcited by 0