Papers with noisy information
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis (2021.findings-emnlp)
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| Challenge: | Recent studies focus on learning cross-modal dynamics, but neglect to explore optimal solution for unimodal networks. |
| Approach: | They propose a new MSA framework to identify contribution of modalities and reduce impact of noisy information. |
| Outcome: | The proposed model outperforms state-of-the-art methods on publicly available datasets. |
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods for aspect-based sentiment classification are susceptible to dependency tree due to noisy information and neglecting rich relation information between words. |
| Approach: | They propose a dependency-based double graph model for aspect-based sentiment classification that incorporates structure, relations and linguistic features into the sentiment text. |
| Outcome: | The proposed model is superior to state-of-the-art methods on four benchmark datasets. |