Initial Explorations in English to Turkish Statistical Machine Translation

Proceedings of the Workshop on Statistical Machine Translation, pages 7–14, New York City, June 2006. c⃝2006 Association for Computational Linguistics Initial Explorations in English to Turkish Statistical Machine Translation ˙Ilknur Durgar El-Kahlout Faculty of Enginering and Natural Sciences Sabancı University Istanbul, 34956, Turkey ilknurdurgar@su.sabanciuniv.edu Kemal Oflazer Faculty of Engineering and Natural Sciences Sabancı University Istanbul, 34956, Turkey oflazer@sabanciuniv.edu Abstract This paper presents some very prelimi- nary results for and problems in develop- ing a statistical machine translation sys- tem from English to Turkish. Starting with a baseline word model trained from about 20K aligned sentences, we explore various ways of exploiting morphological struc- ture to improve upon the baseline sys- tem. As Turkish is a language with com- plex agglutinative word structures, we ex- periment with morphologically segmented and disambiguated versions of the parallel texts in order to also uncover relations be- tween morphemes and function words in one language with morphemes and func- tions words in the other, in addition to re- lations between open class content words. Morphological segmentation on the Turk- ish side also conflates the statistics from allomorphs so that sparseness can be al- leviated to a certain extent. We find that this approach coupled with a simple grouping of most frequent morphemes and function words on both sides improve the BLEU score from the baseline of 0.0752 to 0.0913 with the small training data. We close with a discussion on why one should not expect distortion parameters to model word-local morpheme ordering and that a new approach to handling complex mor- photactics is needed. 1 Introduction The availability of large amounts of so-called par- allel texts has motivated the application of statisti- cal techniques to the problem of machine translation starting with the seminal work at IBM in the early 90’s (Brown et al., 1992; Brown et al., 1993). Statis- tical machine translation views the translation pro- cess as a noisy-channel signal recovery process in which one tries to recover the input “signal” e, from the observed output signal f.1 Early statistical machine translation systems used a purely word-based approach without taking into account any of the morphological or syntactic prop- erties of the languages (Brown et al., 1993). Lim- itations of basic word-based models prompted re- searchers to exploit morphological and/or syntac- tic/phrasal structure (Niessen and Ney, (2004), Lee,(2004), Yamada and Knight (2001), Marcu and Wong (2002), Och and Ney (2004),Koehn et al. (2003), among others.) In the context of the agglutinative languages sim- ilar to Turkish (in at least morphological aspects) , there has been some recent work on translating from and to Finnish with the significant amount of data in the Europarl corpus. Although the BLEU (Pap- ineni et al., 2002) score from Finnish to English is 21.8, the score in the reverse direction is reported as 13.0 which is one of the lowest scores in 11 Eu- ropean languages scores (Koehn, 2005). Also, re- ported from and to translation scores for Finnish are the lowest on average, even with the large …

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