@inproceedings{ai-etal-2023-tecs,
    title = "{T}e{CS}: A Dataset and Benchmark for Tense Consistency of Machine Translation",
    author = "Ai, Yiming  and
      He, Zhiwei  and
      Yu, Kai  and
      Wang, Rui",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.164",
    doi = "10.18653/v1/2023.acl-short.164",
    pages = "1930--1941",
    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.",
}

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.
Ai, Yiming He, Zhiwei Yu, Kai Wang, Rui
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation