问题
When I read the paper "Convolutional Neural Networks for Sentence Classification"-Yoon Kim-New York University, I noticed that the paper implemented the "CNN-non-static" model--A model with pre-trained vectors from word2vec,and all words— including the unknown ones that are randomly initialized, and the pre-trained vectors are fine-tuned for each task. So I just do not understand how the pre-trained vectors are fine-tuned for each task. Cause as far as I know, the input vectors, which are converted from strings by word2vec.bin(pre-trained), just like image matrix, which can not change during training CNN. So, if they can, HOW? Please help me out, Thanks a lot in advance!
回答1:
The word embeddings are weights of the neural network, and can therefore be updated during backpropagation.
E.g. http://sebastianruder.com/word-embeddings-1/ :
Naturally, every feed-forward neural network that takes words from a vocabulary as input and embeds them as vectors into a lower dimensional space, which it then fine-tunes through back-propagation, necessarily yields word embeddings as the weights of the first layer, which is usually referred to as Embedding Layer.
来源：https://stackoverflow.com/questions/40098450/hows-the-input-word2vec-get-fine-tuned-when-training-cnn