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Existing neural models usually predict the tag of the current tokenindependent of the neighboring tags. The popular LSTM-CRF model considers thetag dependencies between every two consecutive tags. However, it is hard forexisting neural models to take longer distance dependencies of tags intoconsideration. The scalability is mainly limited by the complex modelstructures and the cost of dynamic programming during training. In our work, wefirst design a new model called "high order LSTM" to predict multiple tags forthe current token which contains not only the current tag but also the previousseveral tags. We call the number of tags in one prediction as "order". Then wepropose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines loworder and high order LSTMs together. MO-BiLSTM keeps the scalability to highorder models with a pruning technique. We evaluate MO-BiLSTM on all-phrasechunking and NER datasets. Experiment results show that MO-BiLSTM achieves thestate-of-the-art result in chunking and highly competitive results in two NERdatasets.

Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?