Automatically extracting dialog models from conversation transcripts

Abstract

There is a growing need for task-oriented natural language dialog systems that can interact with a user to accomplish a given objective. Recent work on building task-oriented dialog systems have emphasized the need for acquiring taskspecific knowledge from un-annotated conversational data. In our work we acquire task-specific knowledge by defining subtask as the key unit of a task-oriented conversation. We propose an unsupervised, apriori like algorithm that extracts the subtasks and their valid orderings from un-annotated humanhuman conversations. Modeling dialogues as a combination of sub-tasks and their valid orderings easily captures the variability in conversations. It also provides us the ability to map our dialogue model to AIML constructs and therefore use off-the-shelf AIML interpreters to build task-oriented chatbots. We conduct experiments on real world data sets to establish the effectiveness of the sub-task extraction process.We codify the extracted sub-tasks in an AIML knowledge base and build a chatbot using this knowledge base. We also show the usefulness of the chatbot in automatically handling customer requests by performing a user evaluation study. © 2009 IEEE.