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This paper proposes to generalize the variational recurrent neural network(RNN) with variational inference (VI)-based dropout regularization employed forthe long short-term memory (LSTM) cells to more advanced RNN architectures likegated recurrent unit (GRU) and bi-directional LSTM/GRU. The new variationalRNNs are employed for slot filling, which is an intriguing but challenging taskin spoken language understanding. The experiments on the ATIS dataset suggestthat the variational RNNs with the VI-based dropout regularization cansignificantly improve the naive dropout regularization RNNs-based baselinesystems in terms of F-measure. Particularly, the variational RNN withbi-directional LSTM/GRU obtains the best F-measure score.

Variational Inference-Based Dropout in Recurrent Neural Networks for Slot Filling in Spoken Language Understanding