@inproceedings{bjerva-augenstein-2021-typological,
title = "Does Typological Blinding Impede Cross-Lingual Sharing?",
author = "Bjerva, Johannes and
Augenstein, Isabelle",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.38",
doi = "10.18653/v1/2021.eacl-main.38",
pages = "480--486",
abstract = "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.",
}
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%0 Conference Proceedings
%T Does Typological Blinding Impede Cross-Lingual Sharing?
%A Bjerva, Johannes
%A Augenstein, Isabelle
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F bjerva-augenstein-2021-typological
%X 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.
%R 10.18653/v1/2021.eacl-main.38
%U https://aclanthology.org/2021.eacl-main.38
%U https://doi.org/10.18653/v1/2021.eacl-main.38
%P 480-486
Markdown (Informal)
[Does Typological Blinding Impede Cross-Lingual Sharing?](https://aclanthology.org/2021.eacl-main.38) (Bjerva & Augenstein, EACL 2021)
ACL
- Johannes Bjerva and Isabelle Augenstein. 2021. Does Typological Blinding Impede Cross-Lingual Sharing?. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 480–486, Online. Association for Computational Linguistics.