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Task-oriented dialogue (TOD) systems are required to identify key informationfrom conversations for the completion of given tasks. Such information isconventionally specified in terms of intents and slots contained intask-specific ontology or schemata. Since these schemata are designed by systemdevelopers, the naming convention for slots and intents is not uniform acrosstasks, and may not convey their semantics effectively. This can lead to modelsmemorizing arbitrary patterns in data, resulting in suboptimal performance andgeneralization. In this paper, we propose that schemata should be modified byreplacing names or notations entirely with natural language descriptions. Weshow that a language description-driven system exhibits better understanding oftask specifications, higher performance on state tracking, improved dataefficiency, and effective zero-shot transfer to unseen tasks. Following thisparadigm, we present a simple yet effective Description-Driven Dialog StateTracking (D3ST) model, which relies purely on schema descriptions and an"index-picking" mechanism. We demonstrate the superiority in quality, dataefficiency and robustness of our approach as measured on the MultiWOZ(Budzianowski et al.,2018), SGD (Rastogi et al., 2020), and the recent SGD-X(Lee et al., 2021) benchmarks.

Description-Driven Task-Oriented Dialog Modeling