Does typological blinding impede cross-lingual sharing?
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Dokumenter
- Does typological blinding impede cross-lingual sharing
Forlagets udgivne version, 410 KB, PDF-dokument
Bridging the performance gap between high- and low-resource languages has been the focus of much previous work. Typological features from databases such as the World Atlas of Language Structures (WALS) are a prime candidate for this, as such data exists even for very low-resource languages. However, previous work has only found minor benefits from using typological information. Our hypothesis is that a model trained in a cross-lingual setting will pick up on typological cues from the input data, thus overshadowing the utility of explicitly using such features. We verify this hypothesis by blinding a model to typological information, and investigate how cross-lingual sharing and performance is impacted. Our model is based on a cross-lingual architecture in which the latent weights governing the sharing between languages is learnt during training. We show that (i) preventing this model from exploiting typology severely reduces performance, while a control experiment reaffirms that (ii) encouraging sharing according to typology somewhat improves performance.
Originalsprog | Engelsk |
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Titel | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2021 |
Sider | 480-486 |
ISBN (Elektronisk) | 9781954085022 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online Varighed: 19 apr. 2021 → 23 apr. 2021 |
Konference
Konference | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 |
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By | Virtual, Online |
Periode | 19/04/2021 → 23/04/2021 |
Sponsor | Babelscape, Bloomberg Engineering, Facebook AI, Grammarly, LegalForce |
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