NLP基础之——Contextual Word Representations and Pretraining
一、ELMO ELMO的基本思想是利用双向的LSTM结构，对于某个语言模型的目标，在大量文本上进行预训练，从LSTM layer中得到contextual embedding，其中较低层的LSTM代表了比较简单的语法信息，而上层的LSTM捕捉的是依赖于上下文的语义信息。ELMO的全称就是Embeddings from Language Models。对于下游的任务，再将这些不同层的向量线性组合，再
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相关文章
- 1. Contextual Word Representations and Pretraining
- 2. Datawhale 零基础⼊⻔NLP-Task13 Contextual Word Representations and Pretraining
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- 5. 【cs224n-12】Modeling contexts of use: Contextual Representations and Pretraining. ELMo and BERT.
- 6. 【cs224n-11】Contextual Word Representations: BERT
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- 8. 论文笔记《Knowledge Enhanced Contextual Word Representations》
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