Enhancing Local Feature Extraction with Global Representation for Neural Text Classification (D19-1)
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| Challenge: | Existing methods for text classification learn long dependency by deeply stacking or hybrid modeling. |
| Approach: | They propose a global-based local feature extraction architecture with global information incorporated into the local feature extractor. |
| Outcome: | The proposed architecture outperforms the previous best models on eight benchmark datasets. |
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Jianyu Zhao, Zhiqiang Zhan, Qichuan Yang, Yang Zhang, Changjian Hu, Zhensheng Li, Liuxin Zhang, Zhiqiang He
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General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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