Mitigating the knowledge acquisition bottleneck for Hungarian word sense disambiguation using multilingual transformers
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...
Elmentve itt :
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| Testületi szerző: | |
| Dokumentumtípus: | Könyv része |
| Megjelent: |
2021
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| Sorozat: | Magyar Számítógépes Nyelvészeti Konferencia
17 |
| Kulcsszavak: | Nyelvészet - számítógép alkalmazása |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/73359 |
| Tartalmi kivonat: | 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. |
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| Terjedelem/Fizikai jellemzők: | 77-89 |
| ISBN: | 978-963-306-781-9 |