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The linear-chain Conditional Random Field (CRF) model is one of the mostwidely-used neural sequence labeling approaches. Exact probabilistic inferencealgorithms such as the forward-backward and Viterbi algorithms are typicallyapplied in training and prediction stages of the CRF model. However, thesealgorithms require sequential computation that makes parallelizationimpossible. In this paper, we propose to employ a parallelizable approximatevariational inference algorithm for the CRF model. Based on this algorithm, wedesign an approximate inference network that can be connected with the encoderof the neural CRF model to form an end-to-end network, which is amenable toparallelization for faster training and prediction. The empirical results showthat our proposed approaches achieve a 12.7-fold improvement in decoding speedwith long sentences and a competitive accuracy compared with the traditionalCRF approach.

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network