MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 3837-3844. https://doi.org/10.24963/ijcai.2020/531
The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.
Natural Language Processing: Natural Language Semantics
Natural Language Processing: Resources and Evaluation