Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation

Yinuo Guo, Chong Ruan, Junfeng Hu


Abstract
In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.
Anthology ID:
W18-6454
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Venues:
EMNLP | WMT | WS
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
740–745
Language:
URL:
https://www.aclweb.org/anthology/W18-6454
DOI:
10.18653/v1/W18-6454
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6454.pdf