論文閱讀 Multi-Task Deep Neural Networks for Natural Language Understanding
這是今年arXiv上Microsoft Research的一篇文本embedding的文章，原文在Multi-task DNN。 這篇文章的思路很簡單，在BERT的基礎上，用multi-task learning對embedding layer進行fine-tuned，但在GLEU benchmark上的實驗結果非常好，九個NLU任務中八個的表現都有提升，benchmark提升了足足2.2%。 M
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相關文章
- 1. Multi-Task Deep Neural Networks for Natural Language Understanding閱讀筆記
- 2. Multi-Task Deep Neural Networks for Natural Language Understanding【MT-DNN模型】
- 3. 論文閱讀：Deep Neural Networks for Object Detection
- 4. CS224d: Deep Learning for Natural Language Process
- 5. Natural Language Processing[論文合集]
- 6. 文獻閱讀：Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Under
- 7. 論文閱讀：Dual Supervised Learning for Natural Language Understanding and Generation
- 8. Bert：論文閱讀-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 9. 論文閱讀 decaNLP -- The Natural Language Decathlon: Multitask Leaning as Question Answering
- 10. 論文閱讀筆記：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding