Papers with multi-label classifier
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (2024.naacl-long)
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| Challenge: | Existing methods for extracting aspects and opinions from text are incomplete. |
| Approach: | They propose a method for extracting Implicit Aspects with Categories and Opinions with Sentiments using implicit tokens. |
| Outcome: | The proposed method outperforms baseline methods on two public benchmark datasets. |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
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| Challenge: | Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes. |
| Approach: | They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two MIMIC datasets. |
Open-world Multi-label Text Classification with Extremely Weak Supervision (2024.emnlp-main)
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| Challenge: | Similar single-label XWS settings cannot be easily adapted for multi-l label classification. |
| Approach: | They propose a novel method for open-world multi-label text classification under extremely weak supervision where the user provides a brief description without any labels or ground-truth label space. |
| Outcome: | The proposed method exhibits a remarkable increase in ground-truth label space coverage on various datasets. |
SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models (2025.findings-acl)
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| Challenge: | Existing large language models struggle with complex tasks such as factually-grounded reasoning and planning due to inherent training biases, model size constraints, and the quality or diversity of pre-training datasets. |
| Approach: | They propose a novel algorithm to select the most suitable LLMs from a large pool and use it to efficiently generalize and perform tasks. |
| Outcome: | The proposed model outperforms existing ensemble-based baselines and achieves competitive performance with similarly sized top-performing LLMs while maintaining efficiency. |