Abstract: Certain examples described herein provide methods and systems for implementing a conversational agent, e.g. to train a predictive model used by the conversational agent. In examples, text data representing agent messages from a dialogue database are clustered and the clusters are used to generate response templates for use by the conversational agent. The predictive model is trained on training data generated by selectively assigning response templates to agent messages from text dialogues. Examples enable a predictive model to be trained on high quality data sets that are generated automatically from a corpus of historical data. In turn, they enable a natural language interface to be efficiently provided.

Abstract: Certain examples described herein allow feedback to be exchanged between a conversational agent and an operator (so-called “bi-directional” feedback). Certain examples allow an incorrect response template to be indicated by the operator and the conversational agent to compute a contribution for tokens representative of how influential the tokens were in the prediction of the incorrect response template by an applied predictive model. The computed contribution is used to provide further feedback to the operator comprising potential tokens to disassociate with the incorrect response template. The operator then selects the tokens they wish to disassociate and the parameters of the predictive model are adjusted based on this feedback. By repeating this process, an accuracy of a conversational agent, in the form of the response templates that are selectable for a text dialogue, may be improved.

Abstract: Certain examples are described that provide methods and systems for generating templates for use by a conversational agent. These examples enable a natural language interface to be provided. Certain examples cluster user and agent messages from a corpus of text data representing text dialogues. This clustering enables response templates to be generated in a way that takes into account a context in which responses are given. In certain examples, messages that are exchanged between a user and a conversational agent are embedded as numeric arrays based a neural sequence-to-sequence model. Clustering routines are used to group dialogue encodings into one or more response clusters, and these clusters may then be used to generate response templates. The response templates may be used by a conversational agent to prepare a response to a user message.