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• W4292596092 abstract "Deep learning has become most prominent in solving various Natural Language Processing (NLP) tasks including sentiment analysis. However, these techniques require a considerably large amount of annotated corpus, which is not easy to obtain for most of the languages, especially under the scenario of low-resource settings. In this article, we propose a deep multi-task multi-lingual adversarial framework to solve the resource-scarcity problem of sentiment analysis by leveraging the useful and relevant knowledge from a high-resource language. To transfer the knowledge between the different languages, both the languages are mapped to the shared semantic space using cross-lingual word embeddings. We evaluate our proposed architecture on a low-resource language, Hindi, using English as the high-resource language. Experiments show that our proposed model achieves an accuracy of 60.09% for the movie review dataset and 72.14% for the product review dataset. The effectiveness of our proposed approach is demonstrated with significant performance gains over the state-of-the-art systems and translation-based baselines." @default.
• W4292596092 created "2022-08-22" @default.
• W4292596092 creator A5065100828 @default.
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• W4292596092 date "2022-09-23" @default.
• W4292596092 modified "2023-10-03" @default.
• W4292596092 title "Exploring Multi-lingual, Multi-task, and Adversarial Learning for Low-resource Sentiment Analysis" @default.
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• W4292596092 doi "https://doi.org/10.1145/3514498" @default.
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