An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
This work addresses the challenge of creating more efficient and accurate dialog systems for task-oriented applications, though it appears incremental as it builds on existing neural network approaches.
The authors tackled the problem of building an end-to-end trainable neural network model for task-oriented dialog systems, achieving promising results by outperforming prior models in per-response accuracy metrics.
We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.