Transformer-Capsule Model for Intent Detection
Intent recognition is one of the most crucial tasks in NLUsystems, which are nowadays especially important for design-ing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU net-work implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu , and WebApp.
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Datasets
ATIS
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Intent Detection | ATIS | Transformer-Capsule | Accuracy | 98.89 | # 2 |
