en
Proceedings chapter
Open access
English

Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings

Presented at Osaka (Japan), 11 December 2016
Publication date2016
Abstract

Traditional machine translation evaluation metrics such as BLEU and WER have been widely used, but these metrics have poor correlations with human judgements because they badly represent word similarity and impose strict identity matching. In this paper, we propose some modifications to the traditional measures based on word embeddings for these two metrics. The evaluation results show that our modifications significantly improve their correlation with human judgements.

Citation (ISO format)
WANG, Haozhou, MERLO, Paola. Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings. In: Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6). Osaka (Japan). [s.l.] : [s.n.], 2016. p. 33–41.
Main files (1)
Proceedings chapter (Published version)
Identifiers
  • PID : unige:139724
175views
38downloads

Technical informations

Creation08/14/2020 5:03:00 PM
First validation08/14/2020 5:03:00 PM
Update time03/15/2023 10:25:37 PM
Status update03/15/2023 10:25:36 PM
Last indexation02/12/2024 11:54:18 AM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack