Active Learning in Noisy Conditions for Spoken Language Understanding
Hossein Hadian, Hossein Sameti · International Conference on Computational Linguistics · 2014
Active learning has proved effective in many fields of natural language processing. However, in the field of spoken language understanding which is always dealing with noise, no complete comparison between different active learning methods has been done. This paper compares the best known active learning methods in noisy conditions for spoken language understanding. Additionally a new method based on Fisher information named as Weighted Gradient Uncertainty (WGU) is proposed. Furthermore, Strict Local Density (SLD) method is proposed based on a new concept of local density and a new technique of utilizing information density measures. Results demonstrate that both proposed methods outperform the best performance of the previous methods in noisy and noise-free conditions with SLD being superior to WGU slightly.