Papers with Multi-Task Label Embedding

1 papers
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.

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