@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}