Abstract
We propose WMDO, a metric based on distance between distributions in the semantic vector space. Matching in the semantic space has been investigated for translation evaluation, but the constraints of a translation’s word order have not been fully explored. Building on the Word Mover’s Distance metric and various word embeddings, we introduce a fragmentation penalty to account for fluency of a translation. This word order extension is shown to perform better than standard WMD, with promising results against other types of metrics.- Anthology ID:
- W19-5356
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 494–500
- Language:
- URL:
- https://aclanthology.org/W19-5356
- DOI:
- 10.18653/v1/W19-5356
- Cite (ACL):
- Julian Chow, Lucia Specia, and Pranava Madhyastha. 2019. WMDO: Fluency-based Word Mover’s Distance for Machine Translation Evaluation. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 494–500, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- WMDO: Fluency-based Word Mover’s Distance for Machine Translation Evaluation (Chow et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5356.pdf