Dual Learning for Semi-Supervised Natural Language Understanding
TABLE IX: Slot F1 scores and intent accuracies of BERT-based models on the two datasets.
['[BOLD] Method', 'with BERT', '[BOLD] ATIS (10%) Slot', '[BOLD] ATIS (10%) Intent', '[BOLD] SNIPS (5%) Slot', '[BOLD] SNIPS (5%) Intent']
However, it is orthogonal to the investigation of semi-supervised NLU. The results show that a pre-trained BERT model can further enhance our dual semi-supervised NLU as well as the baseline. Although BERT embeddings can bridge the gap between our method and the baseline, the dual semi-supervised NLU still outperforms the baseline significantly.