A Comparative Evaluation of Data-driven Models in Translation Selection of Machine Translation

A Comparative Evaluation of Data-driven Models in Translation Selection of Machine Translation ◦Yu-Seop Kim ⋆ Jeong-Ho Chang ⋆Byoung-Tak Zhang ◦ Ewha Institute of Science and Technology, Ewha Woman’s Univ. Seoul 120-750 Korea, yskim01@ewha.ac.kr∗ ⋆ Schools of Computer Science and Engineering, Seoul National Univ. Seoul 151-742 Korea, {jhchang, btzhang}@bi.snu.ac.kr† Abstract We present a comparative evaluation of two data-driven models used in translation selec- tion of English-Korean machine translation. La- tent semantic analysis(LSA) and probabilistic latent semantic analysis (PLSA) are applied for the purpose of implementation of data-driven models in particular. These models are able to represent complex semantic structures of given contexts, like text passages. Grammatical rela- tionships, stored in dictionaries, are utilized in translation selection essentially. We have used k-nearest neighbor (k-NN) learning to select an appropriate translation of the unseen instances in the dictionary. The distance of instances in k-NN is computed by estimating the similar- ity measured by LSA and PLSA. For experi- ments, we used TREC data(AP news in 1988) for constructing latent semantic spaces of two models and Wall Street Journal corpus for eval- uating the translation accuracy in each model. PLSA selected relatively more accurate transla- tions than LSA in the experiment, irrespective of the value of k and the types of grammatical relationship. 1 Introduction Construction of language associated resources like thesaurus, annotated corpora, machine- readable dictionary and etc. requires high de- gree of cost, since they need much of human- effort, which is also dependent heavily upon hu- man intuition. A data-driven model, however, does not demand any of human-knowledge, knowledge bases, semantic thesaurus, syntac- tic parser or the like. This model represents ∗ He is supported by Brain Korea 21 project performed by Ewha Institute of Science and Technology. † They are supported by Brain Tech. project controlled by Korean Ministry of Science and Technology. latent semantic structure of contexts like text passages. Latent semantic analysis (LSA) (Lan- dauer et al., 1998) and probabilistic latent se- mantic analysis (PLSA) (Hofmann, 2001) fall under the model. LSA is a theory and method for extracting and representing the contextual-usage meaning of words. This method has been mainly used for indexing and relevance estimation in infor- mation retrieval area (Deerwester et al., 1990). And LSA could be utilized to measure the co- herence of texts (Foltz et al., 1998). By applying the basic concept, a vector representation and a cosine computation, to estimate relevance of a word and/or a text and coherence of texts, we could also estimate the semantic similarity between words. It is claimed that LSA repre- sents words of similar meaning in similar ways (Landauer et al., 1998). Probabilistic LSA (PLSA) is based on proba- bilistic mixture decomposition while LSA is on a linear algebra and singular value decomposition (SVD) (Hofmann, 1999b). In contrast to LSA, PLSA’s probabilistic variant has a sound statis- tical foundation and defines a proper generative model of the data. Both two techniques have a same idea which is to map high-dimensional vectors representing text documents, to a …

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