Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach (D19-1)
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| Challenge: | Existing methods for text emotion distribution learning require a large amount of training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. |
| Approach: | They propose a meta-learning approach to learn text emotion distributions from a small sample using tensor decomposition to capture contextual semantic similarity. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a widely used EDL dataset. |
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Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
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