Papers with weakly supervised baselines

2 papers
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)

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Challenge: Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain.
Approach: They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound .
Outcome: The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect.
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)

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Challenge: Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health.
Approach: They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data.
Outcome: The proposed framework outperforms weakly supervised baselines on four benchmark datasets and is able to generate multiple aspect category-sentiment pairs per review sentence.

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