Interactive Word Alignment for Language Engineering

Interactive Word Alignment for Language Engineering Lars Ahrenberg, Magnus Merkel, Michael Petterstedt Department of Computer and Information Science Linkoping University {lah,magme,g -micpel@ida.liu.se Abstract In this paper we report ongoing work on developing an interactive word alignment environment that will assist a user to quickly produce accurate full-coverage word alignment in bitexts for different language engineering tasks, such as MT lexicons and gold standards for evalua- tion. The system uses a graphical inter- face, static and dynamic resources as well as machine learning techniques. We also sketch how the system is being integrated with an automatic word aligner. 1 Introduction Automatic word alignment systems have proved to be useful tools for various language and NLP tasks, such as bilingual lexicon extraction for lexicography, bilingual terminology and machine translation. Although performance is improving, (precision in the range from 80 to 95 per cent, recall slightly above 50%), there are applications, such as translation and the creation of gold stan- dards, where these figures are not good enough. Furthermore, since most automatic systems rely on co-occurrence, rare correspondences go unno- ticed, even though they may be relevant for ap- plications such as terminology or lexicography. This means that even for these applications higher recall and precision will give better effect. For machine translation errors in alignment are likely to cause errors in translation. Thus, either there will have to be a reviewing process when a generated bilingual dictionary is to be part of a MT system, or else the reviewing could be made in the underlying files, i.e., by interactive review- ing. Extending the application area to more lin- guistic fields, such as translation studies or any form of corpus-based linguistics, errors are of course a curse. Also, observations and generalisa- tions would be better grounded if they are com- plete, i.e. all instances of the phenomena of interest have been found and classified. In this paper we present a system and a method to improve the performance of word alignment, by putting a human in the alignment loop. Alignment bears resemblance to translation and, as with translation, systems could improve by learning from human decisions. Kay's argument for the role of humans in translation holds for alignment too; i.e., we should "expect better per- formance of a system that allows human inter- vention as opposed to one that will brook no interference until all the damage has been done" (Kay 1997, p 22). An interactive approach to word alignment re- quires an efficient interface to review, modify and create alignments. The main features of our system are that it proposes alignments to the user on the basis of its resources and that it is able to improve on its performance by learning from the user sentence-by-sentence. 2 Previous work Most word alignment systems that have been presented to date are automatic, exploring the co- occurrences of terms in large parallel corpora to generate translational equivalences among word types. In addition to co-occurrence data, some systems employ linguistic knowledge of varying levels of …

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