Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora

Unsupervised word segmentation (UWS) can provide domain-adaptive segmentation for statistical machine translation (SMT) without annotated data, and bilingual UWS can even optimize segmentation for alignment. Monolingual UWS approaches of explicitly modeling the probabilities of words through Dirichlet process (DP) models or Pitman-Yor process (PYP) models have achieved high accuracy, but their bilingual counterparts have only been carried out on small corpora such as basic travel expression corpus (BTEC) due to the computational complexity. This paper proposes an efficient unified PYP-based monolingual and bilingual UWS method. Experimental results show that the proposed method is comparable to supervised segmenters on the in-domain NIST OpenMT corpus, and yields a 0.96 BLEU relative increase on NTCIR PatentMT corpus which is out-of-domain.