(2019ACL)ERNIE：Enhanced Language Representation with Informative Entities
論文鏈接：https://www.aclweb.org/anthology/P19-1139.pdf 一、Motivation 像BERT這種預訓練的語言模型雖然在很多NLP任務上都取得了不錯的效果，但是BERT只是能讓一句話表達的更通順，並沒有融合外部的知識。而外部的信息實體是可以提高語言表徵能力的。因此這篇論文通過融合知識圖譜（KGs）能讓模型同時利用詞典，語法和知識信息。 二、解決的問題 爲
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相關文章
- 1. ERNIE: Enhanced Language Representation with Informative Entities
- 2. 論文閱讀---ERNIE: Enhanced Language Representation with Informative Entities
- 3. ERNIE-Enhanced Language Representation with Informative Entities 閱讀筆記
- 4. ERNIE: Enhanced Language Representation with Informative Entities 論文研讀
- 5. Extending entities in Symfony2 with Doctrine2
- 6. K-BERT: Enabling Language Representation with Knowledge Graph閱讀筆記
- 7. 論文淺嘗 | K-BERT: Enabling Language Representation with Knowledge Graph
- 8. Language Modeling with N-grams (Speech and Language Processing)
- 9. 《VideoBERT: A Joint Model for Video and Language Representation Learning》
- 10. 【論文筆記】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC