Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining (P18-2)
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Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
| Challenge: | Obtaining high quality labeled data for natural language understanding tasks is slow, error-prone, complicated and expensive. |
| Approach: | They propose a method to blend weak and strong labeled data during the training of neural networks using a topic-dependent evidence detection dataset. |
| Outcome: | The proposed method improves the training of neural networks when a small amount of labeled data is available. |
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