Progressive Sentiment Analysis for Code-Switched Text Data

Sudhanshu Ranjan, Dheeraj Mekala, Jingbo Shang


Abstract
Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition. However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges:(1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can’t be applied efficiently,(2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account. Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples.
Anthology ID:
2022.findings-emnlp.82
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1155–1167
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.82
DOI:
10.18653/v1/2022.findings-emnlp.82
Bibkey:
Cite (ACL):
Sudhanshu Ranjan, Dheeraj Mekala, and Jingbo Shang. 2022. Progressive Sentiment Analysis for Code-Switched Text Data. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1155–1167, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Progressive Sentiment Analysis for Code-Switched Text Data (Ranjan et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.82.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.82.mp4