Following their success of unsupervised SLU, recent studies have also obtained interesting results on the tasks of relation detection Chen et al., 2014a), entity extraction (Wang et al., 2014), and extending domain coverage (El-Kahky et al., 2014;Chen and Rudnicky, 2014).
most of the studies above do not explicitly learn latent factor representations from the data-while we hypothesize that the better robustness in noisy data can be achieved by explicitly modeling the measurement errors (usually produced by automatic speech recognizers (ASR)) using latent variable models and taking additional local and global semantic constraints into account.