[NLP論文閱讀] Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning
論文原文：Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning 引言 這篇文章的想法很有意思，爲了學習到更好的句子表示，作者沒有去設計新的模型來學習句子表示，而是設計了3個預訓練目標來預訓練已有的句子編碼器（Sentence encoder），當模型經過這3個預訓練後在針對目標任務進行訓練
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相關文章
- 1. 論文閱讀——Mockingjay: unsupervised speech representation learning
- 2. 論文閱讀筆記：Momentum Contrast for Unsupervised Visual Representation Learning
- 3. 【論文閱讀】Distributed Representation of Sentence and Document
- 4. [NLP論文閱讀] Supervised Learning of Universal Sentence Representations from NLI data
- 5. Inductive Representation Learning on Large Graphs 論文閱讀
- 6. 論文閱讀——《Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks》
- 7. Momentum Contrast for Unsupervised Visual Representation Learning
- 8. Unsupervised Learning of Edges 論文閱讀
- 9. 小全讀論文《Momentum Contrast for Unsupervised Visual Representation Learning》
- 10. 論文閱讀筆記：Pretraining Methods for Dialog Context Representation Learning