【论文阅读笔记】Supervised Learning of Universal Sentence Representations from Natural Language Inference Da
本文是Facebook AI team发表在EMNLP2018，个人觉得比较有意思。文章主要目的是验证NLP领域做迁移学习的可行性，在多种NLP任务上进行sentence level的embedding，实验得出在NLI任务上进行预训练的表达在迁移学习的效果上是最好的，作者认为这是由于NLI任务使得句子向量中蕴含了句法或知识等对句子含义的理解。文章同时还探索了做NLI迁移学习的不同模
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相关文章
- 1. [NLP论文阅读] Supervised Learning of Universal Sentence Representations from NLI data
- 2. Machine Learning & Deep Learning 论文阅读笔记
- 3. 文献阅读笔记-ALBERT ： A lite BERT for self-supervised learning of language representations
- 4. 论文笔记BERT: Bidirectional Encoder Representations from Transformers
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- 7. 论文阅读：DeepWalk Online Learning of Social Representations
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- 9. Stochastic Answer Networks for Natural Language Inference阅读笔记
- 10. 《Enhanced LSTM for Natural Language Inference》论文总结