Attention-based Sequential Generative Conversational Agent

In this work, we examine the method of enabling computers to understand human interaction by constructing a generative conversational agent. An experimental approach in trying to apply the techniques of natural language processing using recurrent neural networks (RNNs) to emulate the concept of textual entailment or human reasoning is presented. To achieve this functionality, our experiment involves developing an integrated Long Short-Term Memory cell neural network (LSTM) system enhanced with an attention mechanism. The results achieved by the model are shown in terms of the number of epochs versus loss graphs as well as a brief illustration of the model's conversational capabilities.