The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding
Archiki Prasad, Mohammad Ali Rehan, Shreya Pathak, Preethi Jyothi
Abstract
While recent benchmarks have spurred a lot of new work on improving the generalization of pretrained multilingual language models on multilingual tasks, techniques to improve code-switched natural language understanding tasks have been far less explored. In this work, we propose the use of bilingual intermediate pretraining as a reliable technique to derive large and consistent performance gains using code-switched text on three different NLP tasks: Natural Language Inference (NLI), Question Answering (QA) and Sentiment Analysis (SA). We show consistent performance gains on four different code-switched language-pairs (Hindi-English, Spanish-English, Tamil-English and Malayalam-English) for SA and on Hindi-English for NLI and QA. We also present a code-switched masked language modeling (MLM) pretraining technique that consistently benefits SA compared to standard MLM pretraining using real code-switched text.- Anthology ID:
- 2021.mrl-1.16
- Volume:
- Proceedings of the 1st Workshop on Multilingual Representation Learning
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Duygu Ataman, Alexandra Birch, Alexis Conneau, Orhan Firat, Sebastian Ruder, Gozde Gul Sahin
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 176–190
- Language:
- URL:
- https://aclanthology.org/2021.mrl-1.16
- DOI:
- 10.18653/v1/2021.mrl-1.16
- Cite (ACL):
- Archiki Prasad, Mohammad Ali Rehan, Shreya Pathak, and Preethi Jyothi. 2021. The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 176–190, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding (Prasad et al., MRL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.mrl-1.16.pdf
- Data
- SQuAD, TweetEval