Contextual Slot Carryover for Disparate Schemas

Chetan Naik, Arpit Gupta, Hancheng Ge, Mathias Lambert, Ruhi Sarikaya


In the slot-filling paradigm, where a user can refer back to slots in the context during the conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges - scaling to a very large and potentially unbounded set of slot values and dealing with diverse schemas. We present a neural network architecture that addresses the slot value scalability challenge by reformulating the contextual interpretation as a decision to carryover a slot from a set of possible candidates. To deal with heterogenous schemas, we introduce a simple data-driven method for transforming the candidate slots. Our experiments show that our approach can scale to multiple domains and provides competitive results over a strong baseline.


 DOI: 10.21437/Interspeech.2018-1035

Cite as: Naik, C., Gupta, A., Ge, H., Lambert, M., Sarikaya, R. (2018) Contextual Slot Carryover for Disparate Schemas. Proc. Interspeech 2018, 596-600, DOI: 10.21437/Interspeech.2018-1035.


@inproceedings{Naik2018,
  author={Chetan Naik and Arpit Gupta and Hancheng Ge and Mathias Lambert and Ruhi Sarikaya},
  title={Contextual Slot Carryover for Disparate Schemas},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={596--600},
  doi={10.21437/Interspeech.2018-1035},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1035}
}