TITLE: TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation 

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.
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
http://arxiv.org/abs/2305.13740v1