Berend Gábor: Mitigating the knowledge acquisition bottleneck for Hungarian word sense disambiguation using multilingual transformers.
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Abstract
A major hurdle in training all-words word sense disambiguation (WSD) systems for new domains and/or languages is the limited availability of sense annotated training corpora and that their construction is an extremely costly and labor-intensive process. In this paper, we investigate the utilization of multilingual transformer-based language models for performing cross-lingual WSD in the zero-shot setting. Our empirical results suggest that by relying on the intriguing multilingual abilities of pre-trained language models, we can infer reliable sense labels to Hungarian textual utterances in the all-word WSD setting by purely relying on sense-annotated training data in English.
| Item Type: | Conference or Workshop Item |
|---|---|
| Heading title: | Szemantika |
| Journal or Publication Title: | Magyar Számítógépes Nyelvészeti Konferencia |
| Date: | 2021 |
| Volume: | 17 |
| ISBN: | 978-963-306-781-9 |
| Page Range: | pp. 77-89 |
| Language: | English |
| Event Title: | Magyar számítógépes nyelvészeti konferencia (17.) (2021) (Szeged) |
| Related URLs: | http://acta.bibl.u-szeged.hu/73340/ |
| Uncontrolled Keywords: | Nyelvészet - számítógép alkalmazása |
| Additional Information: | Bibliogr.: p. 86-89. és a lábjegyzetekben ; összefoglalás angol nyelven |
| Subjects: | 01. Natural sciences 01. Natural sciences > 01.02. Computer and information sciences 06. Humanities 06. Humanities > 06.02. Languages and Literature |
| Date Deposited: | 2021. Sep. 28. 10:55 |
| Last Modified: | 2022. Nov. 08. 11:49 |
| URI: | http://acta.bibl.u-szeged.hu/id/eprint/73359 |
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