Optimized Uyghur Segmentation for Statistical Machine Translation

Mi, Chenggang; Yang, Ya-Ting Carolyn; Dong, Rui; Zhou, Xi; Wang, Lei; Xiao, Li; Jiang, Tongwen; Osman, Turghun

doi:10.1007/978-3-319-19581-0_36

Cited by 1 publication

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“…These methods can be classified into three categories: unsupervised approaches (Goldsmith, 2001) (Creutz and Lagus, 2002) (Creutz et al, 2007) (Poon et al, 2009) (Abudukelimu et al, 2017), supervised approaches (Sirts andGoldwater, 2013) (Ruokolainen et al, 2013) and semi-supervised approaches (Kohonen et al, 2010) (Ruokolainen et al, 2014) (Tursun et al, 2016). Other people also put their efforts on morphological segmentation for SMT, such as (Lee, 2004) (Grönroos et al, 2016) (Mermer andSaraclar, 2011) (Sereewattana, 2003) (Clifton, 2010) (Bisazza and Federico, 2009) (Al-Haj and Lavie, 2012) (Rasooli et al, 2013) (Mi et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Log-linear Models for Uyghur Segmentation in Spoken Language Translation

Yang

Dong

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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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.

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