Exploring Different Representational Units in English-to-Turkish Statistical Machine Translation

Proceedings of the Second Workshop on Statistical Machine Translation, pages 25–32, Prague, June 2007. c⃝2007 Association for Computational Linguistics Exploring Different Representational Units in English-to-Turkish Statistical Machine Translation Kemal Oflazer†,‡ †Language Technologies Institute Carnegie Mellon University Pittsburgh, PA, 15213, USA oflazer@sabanciuniv.edu ˙Ilknur Durgar El-Kahlout‡ ‡ Faculty of Engineering and Natural Sciences Sabancı University Istanbul, Tuzla, 34956, Turkey ilknurdurgar@su.sabanciuniv.edu Abstract We investigate different representational granularities for sub-lexical representation in statistical machine translation work from English to Turkish. We find that (i) rep- resenting both Turkish and English at the morpheme-level but with some selective morpheme-grouping on the Turkish side of the training data, (ii) augmenting the train- ing data with “sentences” comprising only the content words of the original training data to bias root word alignment, (iii) re- ranking the n-best morpheme-sequence out- puts of the decoder with a word-based lan- guage model, and (iv) using model iteration all provide a non-trivial improvement over a fully word-based baseline. Despite our very limited training data, we improve from 20.22 BLEU points for our simplest model to 25.08 BLEU points for an improvement of 4.86 points or 24% relative. 1 Introduction Statistical machine translation (SMT) from English- to-Turkish poses a number of difficulties. Typo- logically English and Turkish are rather distant lan- guages: while English has very limited morphology and rather fixed SVO constituent order, Turkish is an agglutinative language with a very rich and produc- tive derivational and inflectional morphology, and a very flexible (but SOV dominant) constituent order. Another issue of practical significance is the lack of large scale parallel text resources, with no substan- tial improvement expected in the near future. In this paper, we investigate different represen- tational granularities for sub-lexical representation of parallel data for English-to-Turkish phrase-based SMT and compare them with a word-based base- line. We also employ two-levels of language mod- els: the decoder uses a morpheme based LM while it is generating an n-best list. The n-best lists are then rescored using a word-based LM. The paper is structured as follows: We first briefly discuss issues in SMT and Turkish, and review re- lated work. We then outline how we exploit mor- phology, and present results from our baseline and morphologically segmented models, followed by some sample outputs. We then describe discuss model iteration. Finally, we present a comprehen- sive discussion of our approach and results, and briefly discuss word-repair – fixing morphologicaly malformed words – and offer a few ideas about the adaptation of BLEU to morphologically complex languages like Turkish. 2 Turkish and SMT Our previous experience with SMT into Turkish (Durgar El-Kahlout and Oflazer, 2006) hinted that exploiting sub-lexical structure would be a fruitful avenue to pursue. This was based on the observation that a Turkish word would have to align with a com- plete phrase on the English side, and that sometimes these phrases on the English side could be discontin- uous. Figure 1 shows a pair of English and Turkish sentences that are aligned at the word (top) and mor- …

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