TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
Yiming Ai, Zhiwei He, Kai Yu, Rui Wang
Main: Resources and Evaluation Main-poster Paper
Poster Session 3: Resources and Evaluation (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 09:00-10:30 (EDT) (America/Toronto)
Global Time: July 11, Poster Session 3 (13:00-14:30 UTC)
Keywords:
multilingual corpora
TLDR:
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 correspond...
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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.