Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Table 4: Results of different models on the Ch-En machine translation task. Results of Mixed RNN Li et al. (2017), Bi-Tree-LSTM Chen et al. (2017a) and PKI Zhang et al. (2018) are copied from the original papers.
['TestSet', 'Mixed RNN', 'Bi-Tree-LSTM', 'PKI', 'Seq2Seq +Attn (word)', 'Seq2Seq +Attn (char)', 'Seq2Seq (word) +Attn+BOW', 'Seq2Seq (char) +Attn+BOW']
As can be seen, for the vanilla seq2seq +attention model, the char-based model outperforms the word-based model across all datasets, yielding an average performance boost of +0.83. The same pattern applies to the bag-of-words framework in \newcitema2018bag. When changing the word-based model to the char-based model, we are able to obtain a performance boost of +0.63. As far as we are concerned, this is the best result on this 1.25M Ch-En dataset.