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You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Improving Sentiment Classification in Low-Resource Bengali Language Utilizing Cross-Lingual Self-supervised Learning

Author : Salim Sazzed

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

One of the barriers of sentiment analysis research in low-resource languages such as Bengali is the lack of annotated data. Manual annotation requires resources, which are scarcely available in low-resource languages. We present a cross-lingual hybrid methodology that utilizes machine translation and prior sentiment information to generate accurate pseudo-labels. By leveraging the pseudo-labels, a supervised ML classifier is trained for sentiment classification. We contrast the performance of the proposed self-supervised methodology with the Bengali and English sentiment classification methods (i.e., methods which do not require labeled data). We observe that the self-supervised hybrid methodology improves the macro F1 scores by 15%–25%. The results infer that the proposed framework can improve the performance of sentiment classification in low-resource languages that lack labeled data.

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previous chapter Multi-Step Transfer Learning for Sentiment Analysis
next chapter Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting
Footnotes
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Metadata
Title
Improving Sentiment Classification in Low-Resource Bengali Language Utilizing Cross-Lingual Self-supervised Learning
Author
Salim Sazzed
Copyright Year
2021
Book
Natural Language Processing and Information Systems

Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-80599-9_20

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