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.
@InProceedings{WU18.934, author = {Winston Wu and David Yarowsky}, title = "{Creating Large-Scale Multilingual Cognate Tables}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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