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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Sazzed, S. (2021). Improving Sentiment Classification in Low-Resource Bengali Language Utilizing Cross-Lingual Self-supervised Learning. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_20
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