Abstract
In this paper we introduce MoBiL, a hybrid Monolingual, Bilingual and Language modelling feature set and feature selection and evaluation framework. The set includes translation quality indicators that can be utilized to automatically predict the quality of human translations in terms of content adequacy and language fluency. We compare MoBiL with the QuEst baseline set by using them in classifiers trained with support vector machine and relevance vector machine learning algorithms on the same data set. We also report an experiment on feature selection to opt for fewer but more informative features from MoBiL. Our experiments show that classifiers trained on our feature set perform consistently better in predicting both adequacy and fluency than the classifiers trained on the baseline feature set. MoBiL also performs well when used with both support vector machine and relevance vector machine algorithms.- Anthology ID:
- L16-1581
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 3663–3670
- Language:
- URL:
- https://aclanthology.org/L16-1581
- DOI:
- Cite (ACL):
- Yu Yuan, Serge Sharoff, and Bogdan Babych. 2016. MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3663–3670, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment (Yuan et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/L16-1581.pdf