Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling (D19-1)
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| Challenge: | Existing systems rely on modular, domain-focused frameworks for analyzing complex problems. |
| Approach: | They propose semi-supervised learning methods that can reduce the amount of required intermediate labelling by leveraging un-annotated data instead of transcribed utterances. |
| Outcome: | The proposed model reduces the amount of turn-level annotations by 30% while maintaining equivalent system performance. |
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