Step-by-step guide
Our solution will use modeling and will focus on the future direction of a dialogue agent, so as to generate coherent and interesting dialogue. The model will simulate the dialogue between two virtual agents, with the use of policy gradient methods. These methods are designed to reward the sequences of interaction that display three important properties of conversation: informativeness (non-repeating turns), high coherence, and simplicity in answering (this is related to the forward-looking function). In our solution, an action will be defined as the dialogue or communication utterance that the chatbot generates. Also, a state will be defined as the two previous interaction turns. In order to achieve all of this, we will use the scripts in the following sections.
Data parser
The data parser script is designed to help with the cleaning and preprocessing of our datasets. There are a number of dependencies in this script, such as pickle, codecs, re, OS, time, and numpy. This...
