Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
This work addresses the challenge of building more effective task-oriented dialogue systems for applications like virtual assistants, though it is incremental as it builds on existing models.
The paper tackled the problem of task-oriented dialogue systems by proposing a framework that combines pipeline and sequence-to-sequence models, using a fixed-size distributed representation for dialogue state and attention for knowledge base querying. The result was significant outperformance over sequence-to-sequence baselines on both automatic and human evaluations in experiments on the Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset.
Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.