Denotation Extraction for Interactive Learning in Dialogue Systems
This addresses the challenge of expensive and hard-to-collect training data for dialogue systems, though it appears incremental as it builds on existing extraction methods.
The paper tackles the problem of collecting training data for question answering dialogue systems by introducing a task for denotation extraction from answer hints in human-machine conversations, and presents evaluation results of several models including attention-based neural networks.
This paper presents a novel task using real user data obtained in human-machine conversation. The task concerns with denotation extraction from answer hints collected interactively in a dialogue. The task is motivated by the need for large amounts of training data for question answering dialogue system development, where the data is often expensive and hard to collect. Being able to collect denotation interactively and directly from users, one could improve, for example, natural understanding components on-line and ease the collection of the training data. This paper also presents introductory results of evaluation of several denotation extraction models including attention-based neural network approaches.