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
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 740–745
- Language:
- URL:
- https://aclanthology.org/W18-6454
- DOI:
- 10.18653/v1/W18-6454
- Cite (ACL):
- Yinuo Guo, Chong Ruan, and Junfeng Hu. 2018. Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 740–745, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation (Guo et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W18-6454.pdf