Automatic Identification of Word Translations from Unrelated English and German Corpora

Automatic Identification of Word Translations from Unrelated English and German Corpora Reinhard Rapp University of Mainz, FASK D-76711 Germersheim, Germany rapp @usun2.fask.uni-mainz.de Abstract Algorithms for the alignment of words in translated texts are well established. How- ever, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated texts. This task is more difficult, because most statistical clues useful in the processing of parallel texts cannot be applied to non-par- allel texts. Whereas for parallel texts in some studies up to 99% of the word align- ments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. The current study, which is based on the assumption that there is a correlation between the patterns of word co-occurrences in corpora of different lan- guages, makes a significant improvement to about 72% of word translations identified correctly. 1 Introduction Starting with the well-known paper of Brown et al. (1990) on statistical machine translation, there has been much scientific interest in the alignment of sentences and words in translated texts. Many studies show that for nicely parallel corpora high accuracy rates of up to 99% can be achieved for both sentence and word alignment (Gale & Church, 1993; Kay & R/Sscheisen, 1993). Of course, in practice - due to omissions, transpositions, insertions, and replacements in the process of translation - with real texts there may be all kinds of problems, and therefore ro- bustness is still an issue (Langlais et al., 1998). Nevertheless, the results achieved with these algorithms have been found useful for the corn- pilation of dictionaries, for checking the con- sistency of terminological usage in translations, for assisting the terminological work of trans- lators and interpreters, and for example-based machine translation. By now, some alignment programs are offered commercially: Translation memory tools for translators, such as IBM's Translation Manager or Trados' Translator's Workbench, are bundled or can be upgraded with programs for sentence alignment. Most of the proposed algorithms first con- duct an alignment of sentences, that is, they lo- cate those pairs of sentences that are translations of each other. In a second step a word alignment is performed by analyzing the correspondences of words in each pair of sentences. The algo- rithms are usually based on one or several of the following statistical clues: 1. correspondence of word and sentence order 2. correlation between word frequencies 3. cognates: similar spelling of words in related languages All these clues usually work well for parallel texts. However, despite serious efforts in the compilation of parallel corpora (Armstrong et al., 1998), the availability of a large-enough par- allel corpus in a specific domain and for a given pair of languages is still an exception. Since the acquisition of monolingual corpora is much easier, it would be desirable to have a program that can determine the translations of words from comparable (same domain) or possibly unrelated monolingnal texts of two languages. This is what translators and interpreters usually do …

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