I am new in the NLP field am I have some question about nn.Embedding. I have already seen this post 541, but I’m still confusing with how nn.Embedding generate the vector representation. From the official website and the answer in this post 541. I concluded:

It’s only a lookup table, given the index, it will return the corresponding vector.
The vector representation indicated the weighted matrix is initialized as random values and will be updated by backpropagation.

Now my question are:

If I have 1000 words, using nn.Embedding(1000, 30) to make 30 dimension vectors of each word. Will nn.Embedding generate one-hot vector of each word and create a hidden layer of 30 neuron like word2vec? If so, is it CBOW or Skip-Gram model? What’s the difference between nn.Embedding and nn.Linear

Now, I am researching about the Visual-Question-Answering tasks. Any suggestion about using pretrain vector representation, e.g. word2vec, in question vocabulary or not using it?