User Information Augmented Semantic Frame Parsing Using Progressive Neural Networks

By Yilin Shen, Xiangyu Zeng, Yu Wang, Hongxia Jin. 2018

7 citations recorded by the source corpus.

Semantic frame parsing is a crucial component in spoken language understanding (SLU) to build spoken dialog systems. It consists of two main tasks: intent detection and slot ﬁlling. State-of-the-art deep learning models have demonstrated good results on these tasks. However, these models require not only a large scale annotated training set but also a long training procedure. In this paper, we aim to alleviate these drawbacks for semantic frame parsing by utilizing the ubiquitous user information. We design a novel progressive deep neural network model to incorporate prior knowledge of user information intermediately to better and quickly train a semantic frame parser. Due to the lack of benchmark dataset with real user information, we synthesize the simplest type of user information (location and time) on ATIS benchmark data. The results show that our approach leverages such simple user information to outperform state-of-the-art approaches by 0.25% for intent detection and 0.31% for slot ﬁlling using standard training data. When using smaller training data, the performance improvement on intent detection and slot ﬁlling reaches up to 1.35% and 1.20% respectively. We also show that our approach can achieve similar performance as state-of-the-art approaches by using less than 80% annotated training data. Moreover, the training time to achieve the similar performance is also reduced by over 60%.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “On the other hand, (Ray et al., 2018; Shen et al., 2018b) attempt to personalize the slot ﬁlling model.”