Year: 2018
Type: conference-paper
Abstract: 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.
Authors: Winston Wu, Nidhi Vyas, David Yarowsky
Institutions: Johns Hopkins University, Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2805804830