【论文阅读笔记】Linguistic Knowledge and Transferability of Contextual Representations
本文发布在arxiv 2019 preprint 通过十六种不同的探究任务来研究语境化知识和语境化词语表示的可迁移性。预训练的上下文相关词向量足以在广泛的NLP任务中实现高性能。对于需要特定信息但未被上下文单词表示捕获的任务，学习特定任务的上下文特征有助于在词向量中编码必要的知识。此外，对情境化层的可迁移性模式的分析表明，LSTM的最低层编码最具可迁移的特征，而Transoform
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相关文章
- 1. 论文笔记《Knowledge Enhanced Contextual Word Representations》
- 2. 【论文阅读】 VL-BERT: Pre-training of generic visual-linguistic representations
- 3. Distributed Representations of Sentences and Documents阅读笔记
- 4. 论文阅读：DeepWalk Online Learning of Social Representations
- 5. Distributed Representations of Words and Phrases and their Compositionality论文阅读及实战
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- 8. 【论文笔记-AAAI2020】Overcoming Language Priors in VQA via Decomposed Linguistic Representations
- 9. 论文阅读笔记－CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- 10. Machine Learning & Deep Learning 论文阅读笔记