Papers by Honglun Zhang
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. |
Gated Multi-Task Network for Text Classification (N18-2)
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| 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. |
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. |
Multi-Task Label Embedding for Text Classification (D18-1)
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| Challenge: | Existing work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. |
| Approach: | They propose to combine multi-task learning with semantic vectors to convert labels into vectors . their results are based on extensive experiments on five benchmark datasets based in chinese . |
| Outcome: | The proposed model can improve performance on five benchmark datasets on text classification tasks. |