Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point
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Abstract (arXiv)
We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
6 papers.
- 2021BlonDe: An Automatic Evaluation Metric for Document-level Machine Translationcited by 58
- 2022Extrinsic Evaluation of Machine Translation Metricscited by 14
- 2023Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpuscited by 7
- 2022A Bilingual Parallel Corpus with Discourse Annotationscited by 3
- 2023Evaluating and Improving the Coreference Capabilities of Machine Translation Modelscited by 2
- 2026The Blindness of Document-Level Translation Evaluationcited by 0
- 2026The Blindness of Document-Level Translation Evaluation5 Sep 2026
- 2023Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus18 May 2023
- 2023Evaluating and Improving the Coreference Capabilities of Machine Translation Models16 Feb 2023
- 2022Extrinsic Evaluation of Machine Translation Metrics20 Dec 2022
- 2022A Bilingual Parallel Corpus with Discourse Annotations26 Oct 2022
- 2021BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation22 Mar 2021