A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly.
['Models', 'CTB-5  [ITALIC] F1 [ITALIC] seg', 'CTB-5  [ITALIC] F1 [ITALIC] udep', 'CTB-7  [ITALIC] F1 [ITALIC] seg', 'CTB-7  [ITALIC] F1 [ITALIC] udep', 'CTB-9  [ITALIC] F1 [ITALIC] seg', 'CTB-9  [ITALIC] F1 [ITALIC] udep']
Zhang et al. Kurita et al. Another difference between our joint models and previous works is the combination of POS tags, the previous models all used the POS task as one componential task. Despite the lack of POS tag information, our models still achieve much better results. Other than the performance distinction between previous work, our joint model with or without dependency labels also differ from each other. our joint model with labeled dependency parsing (Joint-Multi) outperforms its counterpart (Joint-Binary) in both Chinese word segmentation and dependency parsing. With respect to the enhancement of dependency parsing caused by the arc labels, we believe it can be credited to two aspects. The first one is the more accurate Chinese word segmentation. The second one is that label information between two characters will give extra supervision for the search of head characters. Owing to the joint decoding of Chinese word segmentation and dependency parsing, we can utilize the character-level pre-trained language model BERT. We presume this indicates BERT can better extract the contextualized information to help the dependency parsing.