Word2Vec (CBOW and Skip-Gram)

I understand CBOW and skip-gram and their respective architectures and the intuition behind the model to a good extent. However I have the following 2 burning questions

1. Consider **CBOW** with **4 context words**, why the input layer has **4 full-vocabulary length one-hot vectors** to represent these 4 words and take average of them? Why can't it be just **1 vocabulary length vector with 4 ones** (in otherwords **4-hot vector**)?
2. **CBOW** takes inputs as context words and predict a …

deeplearning word2vec