NLP基礎之——Contextual Word Representations and Pretraining
一、ELMO ELMO的基本思想是利用雙向的LSTM結構，對於某個語言模型的目標，在大量文本上進行預訓練，從LSTM layer中得到contextual embedding，其中較低層的LSTM代表了比較簡單的語法信息，而上層的LSTM捕捉的是依賴於上下文的語義信息。ELMO的全稱就是Embeddings from Language Models。對於下游的任務，再將這些不同層的向量線性組合，再
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相關文章
- 1. Contextual Word Representations and Pretraining
- 2. Datawhale 零基礎⼊⻔NLP-Task13 Contextual Word Representations and Pretraining
- 3. cs224n學習4： Contextual Word Representations and Pretraining
- 4. 【NLP CS224N筆記】Lecture 13 - Contextual Word Representations and Pretraining
- 5. 【cs224n-12】Modeling contexts of use: Contextual Representations and Pretraining. ELMo and BERT.
- 6. 【cs224n-11】Contextual Word Representations: BERT
- 7. NLP基礎之——Word Vectors and Word Senses
- 8. 論文筆記《Knowledge Enhanced Contextual Word Representations》
- 9. Deep Learning, NLP, and Representations翻譯學習
- 10. NLP-introduction and word vectors