论文笔记《Knowledge Enhanced Contextual Word Representations》
Motivition 作者的出发点有几个： 尽管BERT这种预训练模型取得了state-of-art的成绩。但是、因为他们没有包含真实世界的实体，所以导致这些模型也很难覆盖真实世界的知识。 没有实体没有知识怎么办呢？Knowledge bases、知识库有。 知识库不仅拥有丰富的高质量、人类产生的知识，而且他们包含与原始文本中互补的信息，还能够编码事实性的知识。所以用知识库可以解决因不频繁出现但是
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相关文章
- 1. 【论文阅读笔记】Linguistic Knowledge and Transferability of Contextual Representations
- 2. Contextual Word Representations and Pretraining
- 3. 【论文笔记】ELMo：Deep contextualized word representations
- 4. 【cs224n-11】Contextual Word Representations: BERT
- 5. (论文笔记) Deep contextualized word representations
- 6. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 论文阅读笔记
- 7. CS224n笔记：contextual word representation
- 8. 【论文笔记】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC
- 9. Context-Aware Representations for Knowledge Base Relation Extraction论文笔记
- 10. 【NLP CS224N笔记】Lecture 13 - Contextual Word Representations and Pretraining