Papers › Recurrent Neural Network for Text Classification with Multi-Task Learning
Recurrent Neural Network for Text Classification with Multi-Task Learning
Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | CPED | TextRNN | Accuracy of Sentiment | 47.89 | #10 of 11 | Archive leaderboard | report |
| Emotion Recognition in Conversation | CPED | TextRNN | Macro-F1 of Sentiment | 37.07 | #10 of 11 | Archive leaderboard | report |
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