@InProceedings{mi-EtAl:2017:RANLP,
  author    = {Mi, Chenggang  and  Yang, Yating  and  Dong, Rui  and  Zhou, Xi  and  Wang, Lei  and  Li, Xiao  and  Jiang, Tonghai},
  title     = {Log-linear Models for Uyghur Segmentation in Spoken Language Translation},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {492--500},
  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.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_065}
}