Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems
Table 1: Equal error rates (%) of OOD sentence detection using combinations of sentence representation methods (row) and classification methods (column). The best result in each sentence representation method (row) is underlined; the best result in each classification method (column) is in bold.
['Sentence representation', 'Classification NN-d', 'Classification OSVM', 'Classification CBC', 'Classification IDV', 'Classification Autoencoder', 'Classification Best']
Our proposed method, autoencoder + DC-LSTM IDV + BoW (EER=13.69%) is a previous study (Lane:TASLP2007) that used only ID sentences for training. (Xu:COLING2014) can be applied also to OOD sentence detection, and their method corresponds to autoencoder + DC-RNN w/ non-static (EER=9.16%). This result means that our proposed method decreased EER by 23.37% compared to the state-of-the-art method. In the remainder of this section, we present five detailed observations from the experiment.