Creating Large-Scale Multilingual Cognate Tables

Abstract

Low-resource languages often suffer from a lack of high-coverage lexical resources. In this paper, we propose a method to generate cognate tables by clustering words from existing lexical resources. We then employ character-based machine translation methods in solving the task of cognate chain completion by inducing missing word translations from lower-coverage dictionaries to fill gaps in the cognate chain, finding improvements over single language pair baselines when employing simple but novel multi-language system combination on the Romance and Turkic language families. For the Romance family, we show that system combination using the results of clustering outperforms weights derived from the historical-linguistic scholarship on language phylogenies. Our approach is applicable to any language family and has not been previously performed at such scale. The cognate tables are released to the research community.

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