Dual Learning for Semi-Supervised Natural Language Understanding
TABLE X: Comparison with previous results of NLU on ATIS and SNIPS.
['[BOLD] Method', '[BOLD] Method', '[BOLD] ATIS Slot', '[BOLD] ATIS Intent', '[BOLD] SNIPS Slot', '[BOLD] SNIPS Intent']
Our proposed method (+ dual PL + dual learning) can achieve the state-of-the-art performances on the two datasets, but not significantly outperforming the previous state-of-the-art. We can also find that BERT boosts the performance of ATIS less than SNIPS, which may occur due to the much smaller vocabulary of ATIS. Our method enhanced with BERT gets a decrease of slot F1 score (from 96.4% to 96.0%) and an increase of intent accuracy (from 98.5% to 99.1%) on ATIS, while it achieves a better average score. It shows that our proposed method can also work well in fully supervised settings.