Task-oriented conversational agent self-learning based on sentiment analysis

One of the biggest issues in creating a task-oriented conversational agent with natural language processing based on machine learning comes from size and correctness of the training dataset. It could take months or even years of data collection and the resulting static resource may get soon out of date thus requiring a significant amount of work to supervise it. To overcome these difficulties, we implemented an algorithm with the ability of improving learning efficiency based on the emotions and reactions arising from the conversation between a user and the bot, automatically and in real time. To this end, we have studied an error function that, as in any closed loop control system, corrects the input to improve the output. The proposed method is based on both calibrating the interpretation given to the initial dataset and expanding the dictionary with new terms. Thanks to this innovative approach, the satisfaction of the interlocutors is higher if compared to algorithms with a static dataset or with semi-automatic self-learning rules.