Author:
Wang Yufan,Tang Li,He Tingting
Publisher
Springer International Publishing
Reference28 articles.
1. Shen, B., Inkpen, D.: Speech intent recognition for robots (2017)
2. Liu, B., Lane, I.: Attention-based recurrent neural network models for joint intent detection and slot filling (2016)
3. Liu, B., Line, I.: Recurrent neural network structured output prediction for spoken language understanding (2015)
4. Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)
5. Zhang, X., Wang, H.: A joint model of intent determination and slot filling for spoken language understanding, pp. 5690–5694 (2016)
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