Paper Info
Title
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding.
Abstract
To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function. We also propose a method to identify and label the current speaker to improve the SLU accuracy. In experiments on the benchmark dataset used in Dialog State Tracking Challenge 4, the proposed models achieved significantly higher F1 scores than the state-of-the-art contextual models. Finally, we analyze the effectiveness of the introduced models in detail. The analysis demonstrates that the proposed methods were effective to improve SLU accuracy individually.
Year
Venue
Field
2019
arXiv: Computation and Language
Computer science,Natural language processing,Artificial intelligence,Spoken language
DocType
Volume
Citations 
Journal
abs/1903.08450
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jonggu Kim100.34
Jong-Hyeok Lee274097.88