Neural Chinese Word Segmentation as Sequence to Sequence Translation
Table 3: Experimental results on benchmark datasets w/o pre-training.
['Datasets', 'P', 'R', 'F']
Since our sequence-to-sequence CWS model contains large amount numbers (up to ten million) of free parameters, it is much more likely to be overfitting when training on small datasets To deal with this problem, a large scale pseudo data is utilized to pre-train our model. The Weibo, PKU and MSRA datasets are then used for fine-tuning. Chinese segmentor. The pseudo data contains 12,762,778 sentences in the training set and 4,000 sentences in the validation set and the testing set. The testing set of the pseudo data is used to evaluate the pre-training performance of the model, and the result P, R and F scores are 98.2, 97.1 and 97.7 respectively w.r.t the LTP label as the ground truth.