TALP Phrase-based statistical translation system for European language pairs

Proceedings of the Workshop on Statistical Machine Translation, pages 142–145, New York City, June 2006. c⃝2006 Association for Computational Linguistics TALP Phrase-based statistical translation system for European language pairs Marta R. Costa-juss`a Patrik Lambert Jos´e B. Mari˜no Josep M. Crego Maxim Khalilov Jos´e A. R. Fonollosa Department of Signal Theory and Communications TALP Research Center (UPC) Barcelona 08034, Spain (mruiz,jmcrego,agispert,lambert,khalilov,canton,adrian, rbanchs)@gps.tsc.upc.edu Adri`a de Gispert Rafael E. Banchs Abstract This paper reports translation results for the “Exploiting Parallel Texts for Statis- tical Machine Translation” (HLT-NAACL Workshop on Parallel Texts 2006). We have studied different techniques to im- prove the standard Phrase-Based transla- tion system. Mainly we introduce two re- ordering approaches and add morphologi- cal information. 1 Introduction Nowadays most Statistical Machine Translation (SMT) systems use phrases as translation units. In addition, the decision rule is commonly modelled through a log-linear maximum entropy framework which is based on several feature functions (in- cluding the translation model), hm. Each feature function models the probability that a sentence e in the target language is a translation of a given sen- tence f in the source language. The weights, λi, of each feature function are typically optimized to maximize a scoring function. It has the advantage that additional features functions can be easily in- tegrated in the overall system. This paper describes a Phrase-Based system whose baseline is similar to the system in Costa- juss`a and Fonollosa (2005). Here we introduce two reordering approaches and add morphological information. Translation results for all six trans- lation directions proposed in the shared task are presented and discussed. More specifically, four different languages are considered: English (en), Spanish (es), French (fr) and German (de); and both translation directions are considered for the pairs: EnEs, EnFr, and EnDe. The paper is orga- nized as follows: Section 2 describes the system; 0This work has been supported by the European Union under grant FP6-506738 (TC-STAR project) and the TALP Research Center (under a TALP-UPC-Recerca grant). Section 3 presents the shared task results; and, fi- nally, in Section 4, we conclude. 2 System Description This section describes the system procedure fol- lowed for the data provided. 2.1 Alignment Given a bilingual corpus, we use GIZA++ (Och, 2003) as word alignment core algorithm. During word alignment, we use 50 classes per language estimated by ’mkcls’, a freely-available tool along with GIZA++. Before aligning we work with low- ercase text (which leads to an Alignment Error Rate reduction) and we recover truecase after the alignment is done. In addition, the alignment (in specific pairs of languages) was improved using two strategies: Full verb forms The morphology of the verbs usually differs in each language. Therefore, it is interesting to classify the verbs in order to address the rich variety of verbal forms. Each verb is re- duced into its base form and reduced POS tag as explained in (de Gispert, 2005). This transforma- tion is only done for the alignment, and its goal is to simplify the work of the word alignment im- …

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