TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong Ki Kim, Dongsub Shim, Honglak Lee · 2023
Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications.While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and controllability.To address these challenges, we propose a novel approach focusing on inferring the TOD-Flow graph from dialogue data annotated with dialog acts, uncovering the underlying task structure in the form of a graph.The inferred TOD-Flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability.Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.We show that the proposed TOD-Flow graph better resembles human-annotated graphs compared to prior approaches.Furthermore, when combined with several dialogue policies and end-to-end dialogue models, we demonstrate that our approach significantly improves dialog act classification and end-to-end response generation performance in the Multi-WOZ and SGD benchmarks.Code available