How Grammatical Features Impact Machine Translation: A New Test Suite for Chinese-English MT Evaluation

Huacheng Song, Yi Li, Yiwen Wu, Yu Liu, Jingxia Lin, Hongzhi Xu


Abstract
Machine translation (MT) evaluation has evolved toward a trend of fine-grained granularity, enabling a more precise diagnosis of hidden flaws and weaknesses of MT systems from various perspectives. This paper examines how MT systems are potentially affected by certain grammatical features, offering insights into the challenges these features pose and suggesting possible directions for improvement. We develop a new test suite by extracting 7,848 sentences from a multi-domain Chinese-English parallel corpus. All the Chinese text was further annotated with 43 grammatical features using a semi-automatic method. This test suite was subsequently used to evaluate eight state-of-the-art MT systems according to six different automatic evaluation metrics. The results reveal intriguing patterns of MT performance associated with different domains and various grammatical features, highlighting the test suite’s effectiveness. The test suite was made publicly available and it will serve as an important benchmark for evaluating and diagnosing Chinese-English MT systems.
Anthology ID:
2024.wmt-1.117
Volume:
Proceedings of the Ninth Conference on Machine Translation
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1200–1221
Language:
URL:
https://aclanthology.org/2024.wmt-1.117
DOI:
Bibkey:
Cite (ACL):
Huacheng Song, Yi Li, Yiwen Wu, Yu Liu, Jingxia Lin, and Hongzhi Xu. 2024. How Grammatical Features Impact Machine Translation: A New Test Suite for Chinese-English MT Evaluation. In Proceedings of the Ninth Conference on Machine Translation, pages 1200–1221, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
How Grammatical Features Impact Machine Translation: A New Test Suite for Chinese-English MT Evaluation (Song et al., WMT 2024)
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PDF:
https://aclanthology.org/2024.wmt-1.117.pdf