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Automatic Discovery of Non-Compositional Compounds in Parallel Data (1997)
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@MISC{Melamed97automaticdiscovery,
author = {I. Dan Melamed},
title = {Automatic Discovery of Non-Compositional Compounds in Parallel Data},
year = {1997}
}
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Abstract
Automatic segmentation of text into minimal content-bearing units is an unsolved problem even for languages like English. Spaces between words offer an easy first approximation, but this approximation is not good enough for machine translation (MT), where many word sequences are not translated word-for-word. This paper presents an efficient automatic method for discover- ing sequences of words that are translated as a unit. The method proceeds by comparing pairs of statistical translation models induced from parallel texts in two languages. It can discover hundreds of noncompositional compounds on each iteration, and constructs longer compounds out of shorter ones. Objective evaluation on a simple machine translation task has shown the method's potential to improve the quality of MT output. The method makes few assumptions about the data, so it can be applied to parallel data other than parallel texts, such as word spellings and pronunci- ations.
Keyphrases
non-compositional compound automatic discovery parallel data parallel text many word sequence efficient automatic method word spelling easy first approximation noncompositional compound pronunci ations unsolved problem statistical translation model machine translation discover ing sequence method proceeds objective evaluation minimal content-bearing unit mt output automatic segmentation simple machine translation task
