A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation
Table 2: Performance of the CWS comparison between the schemes
['[BOLD] Scheme', '[BOLD] P', '[BOLD] R', '[BOLD] F']
In generic temporal convolutional network, it focusses on the information at left scope in a sentence (the past scheme). However, in our model, we only focus on the information at the right scope in a sentence (the future scheme). We compare the difference between the schemes for convolutional scope in performance of the CWS on the test set of NLPCC2016. The reason is that starting a dilated convolution from zero paddings by adopting the past scheme will lose the information of characters in front of a sentence. Instead, the future scheme is without the problem that is the loss of the information in front of a sentence.