Hands-On Natural Language Processing with Python

Extending memory networks for dialog modeling

We consider a dialog as a turn-based conversation between two participants (say A and B), where each turn of dialog involves an utterance by A followed by a response by B. We can then treat the production of a response at each turn as an NLU task where we must choose or generate an appropriate response for an incoming query based on the entire conversation history before the query.

We have already discussed how we can build a memory network-based QA model, which takes a question and some associated facts as input, and produces a response to the question by reasoning over the facts. To effectively model dialog as part of such a framework, the utterance at each turn of the conversation would be ...