TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 23 May 2023.
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6 of 22 references.
References · 6
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
- 2020Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metricscited by 158
- 2021Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Paperscited by 71
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