Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization.
['Training Setting Domain ↓ Model →', 'Zero-shot CT', 'Zero-shot RZT', 'Zero-shot Coach', 'Zero-shot +TR', 'Few-shot on 20 (1%) samples CT', 'Few-shot on 20 (1%) samples RZT', 'Few-shot on 20 (1%) samples Coach', 'Few-shot on 20 (1%) samples +TR', 'Few-shot on 50 (2.5%) samples CT', 'Few-shot on 50 (2.5%) samples RZT', 'Few-shot on 50 (2.5%) samples Coach', 'Few-shot on 50 (2.5%) samples +TR']
The CT framework suffers from the difficulty of capturing the whole slot entity, while our framework is able to recognize the slot entity tokens by sharing its parameters across all slot types. Based on the CT framework, the performance of RZT is still limited, and Coach outperforms RZT by a ∼3% F1-score in the zero-shot setting. Additionally, template regularization further improves the adaptation robustness by helping the model cluster the utterance representations into a similar vector space based on their corresponding template representations.