Extending a trained neural network for a larger input I have a seq2seq conversational model (based on this implementation) trained on the Cornell movie dialogs.
Now I want to fine-tune it on a much smaller dataset. The new data comes with the new words, and I want UNKs for as few new words as possible. So I'm going to create a new network with respect to the new input/output sizes, and I'm going to initialize its submatrices with learned weights I have at hand.

Could you say if this method can cause problems with the resulting model's performance? E.g. are the softmaxes likely to be affected significantly with these new initially untrained weights?
And if it's OK, do you have some examples on how to do it with the least pain in tensorflow's seq2seq setup?
Its okay as long as the nerwork you are planning to create has the same number of layers and units i.e the dimensions of your network must be compatible with the weights that you are borrowing from the trained model. Also it would be better if you follow the second blog post of suriyadeepan practical seq2seq where he trains a conversation model on twitter chat. The code is much simpler and easier to understand, also it is on a smaller dataset, also he mentioned that the bot trained on cornell movie dialog corpus wasnt performing so well. Mainly to use the pre-trained weights all you have to do is load the model, create placeholders for the weights, assign thr weights from the loaded model to the placeholders and run a forward pass. This blog and this question might help you with this task