For many of the world's languages, the Bible is the only significant bilingual, or even monolingual, text, making it a unique training resource for tasks such as translation, named entity analysis, and transliteration. Given the Bible's small size, however, the output of standard word alignment tools can be extremely noisy, making downstream tasks difficult. In this work, we develop and release a novel resource of 1129 aligned Bible person and place names across 591 languages, which was constructed and improved using several approaches including weighted edit distance, machine-translation-based transliteration models, and affixal induction and transformation models. Our models outperform a widely used word aligner on 97% of test words, showing the particular efficacy of our approach on the impactful task of broadly multilingual named-entity alignment and translation across a remarkably large number of world languages. We further illustrate the utility of our translation matrix for the multilingual learning of name-related affixes and their semantics as well as transliteration of named entities.
@InProceedings{WU18.770, author = {Winston Wu and Nidhi Vyas and David Yarowsky}, title = "{Creating a Translation Matrix of the Bible’s Names Across 591 Languages}", 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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