Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
Christophe Servan, Alexandre Bérard, Zied Elloumi, Hervé Blanchon, Laurent Besacier
Abstract
This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is made through the enrichment of a well-known MT evaluation metric: METEOR. METEOR enables an approximate match (synonymy or morphological similarity) between an automatic and a reference translation. Our experiments are made in the framework of the Metrics task of WMT 2014. We show that distributed representations are a good alternative to lexico-semanticresources for MT evaluation and they can even bring interesting additional information. The augmented versions of METEOR, using vector representations, are made available on our Github page.- Anthology ID:
- C16-1110
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1159–1168
- Language:
- URL:
- https://aclanthology.org/C16-1110
- DOI:
- Cite (ACL):
- Christophe Servan, Alexandre Bérard, Zied Elloumi, Hervé Blanchon, and Laurent Besacier. 2016. Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1159–1168, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources? (Servan et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/C16-1110.pdf
- Code
- cservan/METEOR-E
- Data
- WMT 2014