Papers with Multi-task Learning
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding (2022.findings-aacl)
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| Challenge: | Meta-Learning requires a large number of training tasks to learn representations that transfer well to unseen tasks. |
| Approach: | They propose a method which synthesizes new tasks by linearly interpolating existing tasks. |
| Outcome: | The proposed method outperforms baselines and does not degrade performance even when it is high. |
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)
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| Challenge: | Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance. |
| Approach: | They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context. |
| Outcome: | The proposed model outperforms state-of-the-art models across five benchmark datasets. |