GUIDE: Creating Semantic Domain Dictionaries for Low-Resource Languages

Over 7,000 of the world's 7,168 living languages are still low-resourced. This paper aims to narrow the language documentation gap by creating multiparallel dictionaries, clustered by SIL's semantic domains. This task is new for machine learning and has previously been done manually by native speakers. We propose GUIDE, a language-agnostic tool that uses a GNN to create and populate semantic domain dictionaries, using seed dictionaries and Bible translations as a parallel text corpus. Our work sets a new benchmark, achieving an exemplary average precision of 60% in eight languages without training data and predicting an average of 2,400 dictionary entries. We share the code, model, multilingual test set, and new dictionaries with the research community.