論文筆記《Knowledge Enhanced Contextual Word Representations》
Motivition 作者的出發點有幾個： 儘管BERT這種預訓練模型取得了state-of-art的成績。但是、因爲他們沒有包含真實世界的實體，所以導致這些模型也很難覆蓋真實世界的知識。 沒有實體沒有知識怎麼辦呢？Knowledge bases、知識庫有。 知識庫不僅擁有豐富的高質量、人類產生的知識，而且他們包含與原始文本中互補的信息，還能夠編碼事實性的知識。所以用知識庫可以解決因不頻繁出現但是
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相關文章
- 1. 【論文閱讀筆記】Linguistic Knowledge and Transferability of Contextual Representations
- 2. Contextual Word Representations and Pretraining
- 3. 【論文筆記】ELMo：Deep contextualized word representations
- 4. 【cs224n-11】Contextual Word Representations: BERT
- 5. (論文筆記) Deep contextualized word representations
- 6. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 論文閱讀筆記
- 7. CS224n筆記：contextual word representation
- 8. 【論文筆記】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC
- 9. Context-Aware Representations for Knowledge Base Relation Extraction論文筆記
- 10. 【NLP CS224N筆記】Lecture 13 - Contextual Word Representations and Pretraining