Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
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