Bilingual Sense Similarity for Statistical Machine Translation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 834–843, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Bilingual Sense Similarity for Statistical Machine Translation Boxing Chen, George Foster and Roland Kuhn National Research Council Canada 283 Alexandre-Taché Boulevard, Gatineau (Québec), Canada J8X 3X7 {Boxing.Chen, George.Foster, Roland.Kuhn}@nrc.ca Abstract This paper proposes new algorithms to com- pute the sense similarity between two units (words, phrases, rules, etc.) from parallel cor- pora. The sense similarity scores are computed by using the vector space model. We then ap- ply the algorithms to statistical machine trans- lation by computing the sense similarity be- tween the source and target side of translation rule pairs. Similarity scores are used as addi- tional features of the translation model to im- prove translation performance. Significant im- provements are obtained over a state-of-the-art hierarchical phrase-based machine translation system. 1 Introduction The sense of a term can generally be inferred from its context. The underlying idea is that a term is characterized by the contexts it co-occurs with. This is also well known as the Distribu- tional Hypothesis (Harris, 1954): terms occurring in similar contexts tend to have similar mean- ings. There has been a lot of work to compute the sense similarity between terms based on their distribution in a corpus, such as (Hindle, 1990; Lund and Burgess, 1996; Landauer and Dumais, 1997; Lin, 1998; Turney, 2001; Pantel and Lin, 2002; Pado and Lapata, 2007). In the work just cited, a common procedure is followed. Given two terms to be compared, one first extracts various features for each term from their contexts in a corpus and forms a vector space model (VSM); then, one computes their similarity by using similarity functions. The fea- tures include words within a surface window of a fixed size (Lund and Burgess, 1996), grammati- cal dependencies (Lin, 1998; Pantel and Lin 2002; Pado and Lapata, 2007), etc. The similari- ty function which has been most widely used is cosine distance (Salton and McGill, 1983); other similarity functions include Euclidean distance, City Block distance (Bullinaria and Levy; 2007), and Dice and Jaccard coefficients (Frakes and Baeza-Yates, 1992), etc. Measures of monolin- gual sense similarity have been widely used in many applications, such as synonym recognizing (Landauer and Dumais, 1997), word clustering (Pantel and Lin 2002), word sense disambigua- tion (Yuret and Yatbaz 2009), etc. Use of the vector space model to compute sense similarity has also been adapted to the mul- tilingual condition, based on the assumption that two terms with similar meanings often occur in comparable contexts across languages. Fung (1998) and Rapp (1999) adopted VSM for the application of extracting translation pairs from comparable or even unrelated corpora. The vec- tors in different languages are first mapped to a common space using an initial bilingual dictio- nary, and then compared. However, there is no previous work that uses the VSM to compute sense similarity for terms from parallel corpora. The sense similarities, i.e. the translation probabilities in …

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