【論文筆記】BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis（2019）
這篇論文提出了將閱讀理解技術用於網絡評論數據，來避免現在電子商務機器人的回答不夠靈活的缺點的一種思路。作者稱這種問題爲RRC(Review Reading Comprehension)，還做了一個小數據集（大概加起來只有2000多條示例數據）。作者這篇論文對我來說最大的價值在於他提出了一種BERT的後訓練方法，讓小數據量下訓練BERT也得到了很好的效果。 1. 介紹 前面針對作者定義的RRC的一系
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相關文章
- 1. 論文筆記--Multi-Style Generative Reading Comprehension (Masque)
- 2. Gated Self-Matching Networks for Reading Comprehension and Question Answering論文筆記
- 3. Gated Self-Matching Networks for Reading Comprehension and Question Answering論文閱讀筆記
- 4. 【論文筆記】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 5. 論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
- 6. 《Review Sentiment Analysis Based on Deep Learning》論文閱讀筆記
- 7. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends
- 8. Attention-over-Attention Neural Networks for Reading Comprehension論文筆記
- 9. 【論文筆記】Simple and Effective Multi-Paragraph Reading Comprehension
- 10. Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension 論文閱讀筆記