Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play
This addresses the need for better interpretability and testing in task-oriented conversational AI, though it is incremental as it builds on existing self-play and memory network methods.
The paper tackles the problem of evaluating neural conversational agents by creating challenge datasets through dialogue self-play, showing that different memory network architectures perform variably on out-of-pattern test cases.
End-to-end neural approaches are becoming increasingly common in conversational scenarios due to their promising performances when provided with sufficient amount of data. In this paper, we present a novel methodology to address the interpretability of neural approaches in such scenarios by creating challenge datasets using dialogue self-play over multiple tasks/intents. Dialogue self-play allows generating large amount of synthetic data; by taking advantage of the complete control over the generation process, we show how neural approaches can be evaluated in terms of unseen dialogue patterns. We propose several out-of-pattern test cases each of which introduces a natural and unexpected user utterance phenomenon. As a proof of concept, we built a single and a multiple memory network, and show that these two architectures have diverse performances depending on the peculiar dialogue patterns.