Decoupling Intent and Slot Representations to Mitigate Error Propagation in Cross-Lingual Out-of-Domain Spoken Language

Abstract

Abstract: In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain. The pipeline approach, however, has some disadvantages: error propagation and lack of information sharing. To address these issues, we present a unified neural network that jointly performs domain, intent, and slot predictions. Our approach adopts a principled architecture for multitask learning to fold in the state-of-the-art models for each task. With a few more ingred

Research Question

To what extent does decoupling intent and slot representations in cross-lingual transfer learning reduce error propagation in out-of-domain spoken language understanding tasks?

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