| Challenge: | Existing approaches to multitask learning share the features without distinguishing the usefulness of the features, generating undesired interference between tasks. |
| Approach: | They propose to introduce a gate mechanism into multi-task CNN and propose a new gated sharing unit which can filter the feature flows between tasks and greatly reduce the interference. |
| Outcome: | The proposed approach can learn selection rules automatically and gain a great improvement over strong baselines. |
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Learning What to Share: Leaky Multi-Task Network for Text Classification (C18-1)
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| Challenge: | Existing approaches to multi-task learning suffer from the interference between tasks because they lack selection mechanism for feature sharing. |
| Approach: | They propose a multi-task convolutional neural network with the Leaky Unit which has memory and forgetting mechanism to filter the feature flows between tasks. |
| Outcome: | The proposed model can filter feature flows between tasks and improve performance on five datasets. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)
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| Challenge: | Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance. |
| Approach: | They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem. |
| Outcome: | The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
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Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
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. |
| Approach: | They propose a Tchebycheff procedure to optimize multi-task classification problems without convex assumption. |
| Outcome: | The proposed method is able to find an arbitrary Pareto optimal solution in the PareTO set if the problem is convex, but excludes many Paret optimal solutions from its search scope. |
Adaptive Convolution for Text Classification (N19-1)
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| Challenge: | Existing convolutional neural networks (CNNs) use sparse representations of text, such as bag-of-words. |
| Approach: | They propose an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs) they attach filter-generating networks to convevolution blocks in existing CNNs . |
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Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
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Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)
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| Challenge: | Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. |
| Approach: | They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks. |
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A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
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| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |