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.
MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation / Barba, Edoardo; Procopio, Luigi; Campolungo, Niccolò; Pasini, Tommaso; Navigli, Roberto. - (2020), pp. 3837-3844. ((Intervento presentato al convegno Twenty-Ninth International Joint Conference on Artificial Intelligence tenutosi a Kyoto, Japan [10.24963/ijcai.2020/531].
Titolo: | MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation | |
Autori: | ||
Data di pubblicazione: | 2020 | |
Citazione: | MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation / Barba, Edoardo; Procopio, Luigi; Campolungo, Niccolò; Pasini, Tommaso; Navigli, Roberto. - (2020), pp. 3837-3844. ((Intervento presentato al convegno Twenty-Ninth International Joint Conference on Artificial Intelligence tenutosi a Kyoto, Japan [10.24963/ijcai.2020/531]. | |
Handle: | http://hdl.handle.net/11573/1431920 | |
ISBN: | 978-0-9992411-6-5 | |
Appartiene alla tipologia: | 04b Atto di convegno in volume |
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