Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
Table 3: Effect of pruning on speeding up the decoding.
['[BOLD] Model', '[BOLD] All-Chunking Time (s)', '[BOLD] All-Chunking F1', '[BOLD] English-NER Time (s)', '[BOLD] English-NER F1', '[BOLD] Dutch-NER Time (s)', '[BOLD] Dutch-NER F1']
As shown, the pruning technique has shown a great ability to save time with no loss of accuracy. We then give a detailed analysis of the pruning technique. Original search process of dynamic programming considers all possible high order dependencies. However, most low-order tags have been assigned very low probabilities by low-order models and they will form almost impossible high-order tags. Thus, we only keep a small subset of all low-order tags, which makes the possible combinations shrink rapidly so that the cost of dynamic programing is greatly reduced. We also find that the pruned search space has no effect on the performance of the models. We suppose it is almost unlikely that the best tag sequence is out of the pruned search space. Hence, the accuracy is kept to the full extent, as shown in our experiments.