Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping
Table 2: Performance Comparison with Other Parsing Models
['[EMPTY]', '[BOLD] UAS', '[BOLD] LAS']
The top four rows are performance tested on our Chinese corpus and the bottom two on SciDTB. From Human(English) and Human(Chinese), we can see that discourse labeling is a difficult task for both languages. Our Zero-shot method significantly outperforms the Random parser, meaning that parallel English and Chinese texts have similar discourse structures, and that our method effectively leverages such information. Zero-shot also performs about 12% and 11% higher than the Supervised(Chinese) with respect to the UAS and LAS metrics, because our corpus is too small to support supervision well enough. Compared with Supervised(English), the performance of Zero-shot is acceptable in terms of identifying the head EDU, but barely satisfactory in labeling the relations, which might be explained by different statistical distributions of relations types in Chinese and English..