Template generation for a conversational agent

A system for implementing a text dialogue, comprising:

• a conversational agent comprising at least a processor and a memory to receive one or more user messages from a client device over a network and send agent messages in response to the one or more user messages;
• a template database comprising response templates for use by the conversational agent to generate agent messages;
• a dialogue database comprising historical data representing a plurality of text dialogues, each text dialogue comprising a sequence of messages exchanged between a user and a conversational agent, each message comprising text data;
• a trained sequence-to-sequence model comprising an encoder and a decoder, the encoder receiving a sequence of tokens as an input and encoding the sequence of tokens as a numeric array, the decoder receiving the numeric array and generating a sequence of tokens as an output, the sequence-to-sequence model being trained using text from a dialogue prefix as the input, the dialogue prefix comprising at least a subset of the sequence of messages in a given text dialogue, and text from a next message following the dialogue prefix as a ground truth for comparison with the output;
• a dialogue encoding generator comprising at least a processor and a memory to generate a dialogue encoding for agent response messages within the plurality of text dialogues using at least dialogue prefix encodings output by the encoder of the trained neural sequence-to-sequence model;
• a clustering engine comprising at least a processor and a memory to group the plurality of dialogue encodings into one or more response clusters and output a representative dialogue encoding for each response cluster;
• and
• a response template generator comprising at least a processor and a memory to receive representative dialogue encodings for the one or more response clusters and to store message text from the agent messages associated with each representative dialogue encoding as a response template in the template database.

A non-transient, computer-readable medium comprising computer program instructions that, when executed, cause a processor to:

• obtain historical data representing a plurality of text dialogues, each text dialogue comprising a sequence of messages exchanged between a user and a conversational agent, each message comprising text data;
• train a neural sequence-to-sequence model using the historical data,
• the neural sequence-to-sequence model comprising an encoder and a decoder, the encoder receiving a sequence of tokens as an input and encoding the sequence of tokens as a numeric array, the decoder receiving the numeric array and generating a sequence of tokens as an output,
• the instructions including instructions to, for a given text dialogue:
• use text from a dialogue prefix as the input to the neural sequence-to-sequence model, the dialogue prefix comprising at least a subset of the sequence of messages in the given text dialogue, and
• use text from a next message following the dialogue prefix as a ground truth for comparison with the output of the neural sequence-to-sequence model;
• generate dialogue encodings for agent response messages within the plurality of text dialogues using at least dialogue prefix encodings output by the encoder of the trained neural sequence-to-sequence model;
• apply a clustering routine to group the plurality of dialogue encodings into one or more response clusters;
• select a representative dialogue encoding for each cluster;
• and
• store message text from the agent response messages associated with each representative dialogue encoding as a response template,
• wherein the response templates are supplied to a conversational agent for use in generating an agent response message during a text dialogue.