A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Table 5: Ablation experiments for Joint-Multi models.
['Models', 'CTB-5  [ITALIC] F1 [ITALIC] seg', 'CTB-5  [ITALIC] F1 [ITALIC] udep', 'CTB-5  [ITALIC] UAS', 'CTB-5  [ITALIC] F1 [ITALIC] ldep', 'CTB-5  [ITALIC] LAS', 'CTB-7  [ITALIC] F1 [ITALIC] seg', 'CTB-7  [ITALIC] F1 [ITALIC] udep', 'CTB-7  [ITALIC] UAS', 'CTB-7  [ITALIC] F1 [ITALIC] ldep', 'CTB-7  [ITALIC] LAS', 'CTB-9  [ITALIC] F1 [ITALIC] seg', 'CTB-9  [ITALIC] F1 [ITALIC] udep', 'CTB-9  [ITALIC] UAS', 'CTB-9  [ITALIC] F1 [ITALIC] ldep', 'CTB-9  [ITALIC] LAS']
As our model uses various n-gram pre-trained embeddings, we explore the influence of these pre-trained embeddings. We also tried to remove the bigram and trigram. Compared with the Joint-Multi model, without bigram and trigram, it performs worse in all metrics. However, the comparison between the second row and the third row shows divergence in Chinese word segmentation and dependency parsing for dataset CTB-5 and CTB-7. For Chinese word segmentation, the model without pre-trained embeddings gets superior performance than without bigram and trigram feature. While for all dependency parsing related metrics, the model with pre-trained character embedding gets better performance. We assume the n-gram features are important to Chinese word segmentation. But for the dependency parsing task, the relation between two characters are more beneficial, when pre-trained embeddings are combined, the model can exploit the relationship encoded in the pre-trained embeddings. Additionally, for CTB-5 and CTB-7, even though the third row has inferior F1seg (in average 0.16% lower than the second row), it still achieves much better F1udep (in average 0.95% higher than the second row). We believe this is a proof that joint Chinese word segmentation and dependency parsing is resistant to error propagation. The higher segmentation and dependency parsing performance for the model without pre-trained embedding in CTB-9 might owing to its large training set, which can achieve better results even from randomly initialized embeddings.