A Conversational Agent Powered by Online Learning

In this work, we improve the performance of a dialogue engine, Say Something Smart, using online learning. Given a request by a user, this engine selects an answer from a corpus of movie subtitles, weighting the quality of each candidate answer according to several criteria and selecting the one that is chosen by the most representative criteria. We contribute with an online approach, using sequential learning, that adjusts the weights of the different criteria using a reference corpus of actual dialogues as input to simulate user feedback. This approach effectively allowed Say Something Smart to improve its performance at each interaction, as shown in an experiment performed in a test corpus.