Meta-Learning for Effective Multi-task and Multilingual Modelling

Ishan Tarunesh, Sushil Khyalia, Vishwajeet Kumar, Ganesh Ramakrishnan, Preethi Jyothi


Abstract
Natural language processing (NLP) tasks (e.g. question-answering in English) benefit from knowledge of other tasks (e.g., named entity recognition in English) and knowledge of other languages (e.g., question-answering in Spanish). Such shared representations are typically learned in isolation, either across tasks or across languages. In this work, we propose a meta-learning approach to learn the interactions between both tasks and languages. We also investigate the role of different sampling strategies used during meta-learning. We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset. Our meta-learned model clearly improves in performance compared to competitive baseline models that also include multi-task baselines. We also present zero-shot evaluations on unseen target languages to demonstrate the utility of our proposed model.
Anthology ID:
2021.eacl-main.314
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3600–3612
Language:
URL:
https://aclanthology.org/2021.eacl-main.314
DOI:
10.18653/v1/2021.eacl-main.314
Bibkey:
Cite (ACL):
Ishan Tarunesh, Sushil Khyalia, Vishwajeet Kumar, Ganesh Ramakrishnan, and Preethi Jyothi. 2021. Meta-Learning for Effective Multi-task and Multilingual Modelling. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3600–3612, Online. Association for Computational Linguistics.
Cite (Informal):
Meta-Learning for Effective Multi-task and Multilingual Modelling (Tarunesh et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.314.pdf
Code
 ishan00/meta-learning-for-multi-task-multilingual