ACL2023

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

Yiming Ai, Zhiwei He, Kai Yu, Rui Wang

1 citation

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 1 . 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.