Language Models for Machine Translation: Original vs. Translated Texts

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 363–374, Edinburgh, Scotland, UK, July 27–31, 2011. c⃝2011 Association for Computational Linguistics Language Models for Machine Translation: Original vs. Translated Texts Gennadi Lembersky and Noam Ordan and Shuly Wintner Department of Computer Science, University of Haifa, 31905 Haifa, Israel glembers@campus.haifa.ac.il, noam.ordan@gmail.com, shuly@cs.haifa.ac.il Abstract We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the tar- get language. Corroborating established observations of Translation Studies, we demonstrate that the latter are signifi- cantly better predictors of translated sen- tences than the former, and hence fit the reference set better. Furthermore, trans- lated texts yield better language mod- els for statistical machine translation than original texts. 1 Introduction Statistical machine translation (MT) uses large target language models (LMs) to improve the fluency of generated texts, and it is commonly assumed that for constructing language mod- els, “more data is better data” (Brants and Xu, 2009). Not all data, however, are created the same. In this work we explore the differences be- tween LMs compiled from texts originally writ- ten in the target language and LMs compiled from translated texts. The motivation for our work stems from much research in Translation Studies that suggests that original texts are significantly different from translated ones in various aspects (Geller- stam, 1986). Recently, corpus-based compu- tational analysis corroborated this observation, and Kurokawa et al. (2009) apply it to sta- tistical machine translation, showing that for an English-to-French MT system, a transla- tion model trained on an English-translated-to- French parallel corpus is better than one trained on French-translated-to-English texts. Our re- search question is whether a language model compiled from translated texts may similarly improve the results of machine translation. We test this hypothesis on several translation tasks, where the target language is always En- glish. For each language pair we build two En- glish language models from two types of corpora: texts originally written in English, and human translations from the source language into En- glish. We show that for each language pair, the latter language model better fits a set of refer- ence translations in terms of perplexity. We also demonstrate that the differences between the two LMs are not biased by content but rather reflect differences on abstract linguistic features. Research in Translation Studies suggests that all translated texts, irrespective of source lan- guage, share some so-called translation univer- sals. Consequently, translated texts from sev- eral languages to a single target language resem- ble each other along various axes. To test this hypothesis, we compile additional English LMs, this time using texts translated to English from languages other than the source. Again, we use perplexity to assess the fit of these LMs to refer- ence sets of translated-to-English sentences. We show that these LMs depend on the source lan- guage and differ from each other. Whereas they outperform original-based LMs, LMs compiled from texts that were translated from the source language still …

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