Papers with auxiliary utterance generation task
Improving Limited Labeled Dialogue State Tracking with Self-Supervision (2020.findings-emnlp)
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| Challenge: | Existing dialogue state tracking models require plenty of labeled data, but collecting labels is expensive. |
| Approach: | They propose to use only 1% labeled data to train dialogue state tracking models . they encourage a model to have consistent latent distributions given a perturbed input . |
| Outcome: | The proposed self-supervised signals improve goal accuracy by 8.95% when only 1% labeled data is used on the MultiWOZ dataset. |