Log-linear Models for Uyghur Segmentation in Spoken Language Translation
Chenggang Mi, Yating Yang, Rui Dong, Xi Zhou, Lei Wang, Xiao Li, Tonghai Jiang
Abstract
To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.- Anthology ID:
- R17-1065
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 492–500
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_065
- DOI:
- 10.26615/978-954-452-049-6_065
- Cite (ACL):
- Chenggang Mi, Yating Yang, Rui Dong, Xi Zhou, Lei Wang, Xiao Li, and Tonghai Jiang. 2017. Log-linear Models for Uyghur Segmentation in Spoken Language Translation. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 492–500, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Log-linear Models for Uyghur Segmentation in Spoken Language Translation (Mi et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_065