SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems


  • Harrison Lee Google Research
  • Raghav Gupta Google Research
  • Abhinav Rastogi Google Research
  • Yuan Cao Google Research
  • Bin Zhang Google Research
  • Yonghui Wu Google Research



Speech & Natural Language Processing (SNLP)


Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.




How to Cite

Lee, H., Gupta, R., Rastogi, A., Cao, Y., Zhang, B., & Wu, Y. (2022). SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 10938-10946.



AAAI Technical Track on Speech and Natural Language Processing