BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)
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| Challenge: | Existing methods to regularize task variance are unexplored in multi-task text classification. |
| Approach: | They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm. |
| Outcome: | The proposed method achieves state-of-the-art in multi-task text classification. |
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
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Tchebycheff Procedure for Multi-task Text Classification (2020.acl-main)
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| Challenge: | Existing methods for text classification assume that multitask text classification problems are convex multiobjective optimization problems. |
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MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
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Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)
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Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)
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Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries (2025.acl-long)
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| Challenge: | Existing adversarial text attacks rely on abundant access to shared internal features and numerous queries, limited to a single task type. |
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