Determining the placement of German verbs in English–to–German SMT

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 726–735, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Determining the placement of German verbs in English–to–German SMT Anita Gojun Alexander Fraser Institute for Natural Language Processing University of Stuttgart, Germany {gojunaa, fraser}@ims.uni-stuttgart.de Abstract When translating English to German, exist- ing reordering models often cannot model the long-range reorderings needed to gen- erate German translations with verbs in the correct position. We reorder English as a preprocessing step for English-to-German SMT. We use a sequence of hand-crafted reordering rules applied to English parse trees. The reordering rules place English verbal elements in the positions within the clause they will have in the German transla- tion. This is a difficult problem, as German verbal elements can appear in different po- sitions within a clause (in contrast with En- glish verbal elements, whose positions do not vary as much). We obtain a significant improvement in translation performance. 1 Introduction Phrase-based SMT (PSMT) systems translate word sequences (phrases) from a source language into a target language, performing reordering of target phrases in order to generate a fluent target language output. The reordering models, such as, for example, the models implemented in Moses (Koehn et al., 2007), are often limited to a cer- tain reordering range since reordering beyond this distance cannot be performed accurately. This re- sults in problems of fluency for language pairs with large differences in constituent order, such as English and German. When translating from English to German, verbs in the German output are often incorrectly left near their position in En- glish, creating problems of fluency. Verbs are also often omitted since the distortion model cannot move verbs to positions which are licensed by the German language model, making the translations difficult to understand. A common approach for handling the long- range reordering problem within PSMT is per- forming syntax-based or part-of-speech-based (POS-based) reordering of the input as a prepro- cessing step before translation (e.g., Collins et al. (2005), Gupta et al. (2007), Habash (2007), Xu et al. (2009), Niehues and Kolss (2009), Katz- Brown et al. (2011), Genzel (2010)). We reorder English to improve the translation to German. The verb reordering process is im- plemented using deterministic reordering rules on English parse trees. The sequence of reorderings is derived from the clause type and the composi- tion of a given verbal complex (a (possibly dis- contiguous) sequence of verbal elements in a sin- gle clause). Only one rule can be applied in a given context and for each word to be reordered, there is a unique reordered position. We train a standard PSMT system on the reordered English training and tuning data and use it to translate the reordered English test set into German. This paper is structured as follows: in section 2, we outline related work. In section 3, English and German verb positioning is described. The reordering rules are given in section 4. In sec- tion …

Content truncated to 500 words