(2019ACL)ERNIE：Enhanced Language Representation with Informative Entities

时间 2021-01-13

标签自然语言处理繁體版

原文原文链接

论文链接：https://www.aclweb.org/anthology/P19-1139.pdf 一、Motivation 像BERT这种预训练的语言模型虽然在很多NLP任务上都取得了不错的效果，但是BERT只是能让一句话表达的更通顺，并没有融合外部的知识。而外部的信息实体是可以提高语言表征能力的。因此这篇论文通过融合知识图谱（KGs）能让模型同时利用词典，语法和知识信息。二、解决的问题为

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1. ERNIE: Enhanced Language Representation with Informative Entities
2. 论文阅读---ERNIE: Enhanced Language Representation with Informative Entities
3. ERNIE-Enhanced Language Representation with Informative Entities 阅读笔记
4. ERNIE: Enhanced Language Representation with Informative Entities 论文研读
5. Extending entities in Symfony2 with Doctrine2
6. K-BERT: Enabling Language Representation with Knowledge Graph阅读笔记
7. 论文浅尝 | 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. 【论文笔记】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC
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1. ERNIE: Enhanced Language Representation with Informative Entities
2. 论文阅读---ERNIE: Enhanced Language Representation with Informative Entities
3. ERNIE-Enhanced Language Representation with Informative Entities 阅读笔记
4. ERNIE: Enhanced Language Representation with Informative Entities 论文研读
5. Extending entities in Symfony2 with Doctrine2
6. K-BERT: Enabling Language Representation with Knowledge Graph阅读笔记
7. 论文浅尝 | 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. 【论文笔记】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC

>>更多相关文章<<