CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking

Understand

Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data.

• Existing few-shot methods mainly transfer knowledge learned from external labeled dialogue data (e.g., from question answering, dialogue summarization, machine reading comprehension tasks, etc.) into DST, whereas collecting a large amount of external labeled data is laborious, and the external data may not effectively contribute to the DST-specific task.
• In this paper, we propose a few-shot DST framework called CSS, which Combines Self-training and Self-supervised learning methods.
• The unlabeled data of the DST task is incorporated into the self-training iterations, where the pseudo labels are predicted by a DST model trained on limited labeled data in advance.