Abstract
Tense inconsistency frequently occurs in machine translation. However, there are few criteria to assess the model’s mastery of tense prediction from a linguistic perspective. In this paper, we present a parallel tense test set, containing French-English 552 utterances. We also introduce a corresponding benchmark, tense prediction accuracy. With the tense test set and the benchmark, researchers are able to measure the tense consistency performance of machine translation systems for the first time.- Anthology ID:
- 2023.acl-short.164
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1930–1941
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.164
- DOI:
- 10.18653/v1/2023.acl-short.164
- Cite (ACL):
- Yiming Ai, Zhiwei He, Kai Yu, and Rui Wang. 2023. TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1930–1941, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (Ai et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.acl-short.164.pdf