NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
This addresses the data scarcity issue for task-oriented dialogue systems, enabling more efficient zero-shot domain transfer learning, though it is incremental as it builds on existing simulation and annotation methods.
The paper tackles the problem of collecting task-oriented dialogue data by proposing NeuralWOZ, a model-based simulation framework that generates and annotates dialogues, achieving a new state-of-the-art with improvements of 4.4% joint goal accuracy on average across domains and 5.7% zero-shot coverage against the MultiWOZ 2.1 dataset.
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generates dialogues from (1) user's goal instructions, which are the user context and task constraints in natural language, and (2) system's API call results, which is a list of possible query responses for user requests from the given knowledge base. Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results. We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking. In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with improvements of 4.4% point joint goal accuracy on average across domains, and improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset.