  The linear-chain Conditional Random Field (CRF) model is one of the most
widely-used neural sequence labeling approaches. Exact probabilistic inference
algorithms such as the forward-backward and Viterbi algorithms are typically
applied in training and prediction stages of the CRF model. However, these
algorithms require sequential computation that makes parallelization
impossible. In this paper, we propose to employ a parallelizable approximate
variational inference algorithm for the CRF model. Based on this algorithm, we
design an approximate inference network that can be connected with the encoder
of the neural CRF model to form an end-to-end network, which is amenable to
parallelization for faster training and prediction. The empirical results show
that our proposed approaches achieve a 12.7-fold improvement in decoding speed
with long sentences and a competitive accuracy compared with the traditional
CRF approach.
