Does Typological Blinding Impede Cross-Lingual Sharing?
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
|Title of host publication||Proceedings of The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)|
|Publisher||Association for Computational Linguistics|
|Publication status||Accepted/In press - 2021|
|Event||The 16th Conference of the European Chapter|
of the Association for Computational Linguistics: EACL 2021 -
Duration: 21 Apr 2021 → 23 Apr 2021
Conference number: 16
|Conference||The 16th Conference of the European Chapter|
of the Association for Computational Linguistics
|Periode||21/04/2021 → 23/04/2021|