Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation

Abstract

In the widely-connected digital world, multilingual lexical resources are one of the most important resources, for natural language processing applications, including information retrieval, question answering or knowledge management. These applications benefit from the multilingual knowledge as well as from the semantic relation between the words documented in these resources. Since multilingual dictionary creation and curation is a time-consuming task, we explored the use of multi-way neural machine translation trained on corpora of languages from the same family and trained additionally with a relatively small human-validated dictionary to infer new translation candidates. Our results showed not only that new dictionary entries can be identified and extracted from the translation model, but also that the expected precision and recall of the resulting dictionary can be adjusted by using different thresholds.