A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
Table 4: Performance over different DT levels. (“↓”- Top down approach, “↑”- Bottom up approach)
['Height', 'Std', 'Bare ↓', 'Bare ↑', 'Nuc ↓', 'Nuc ↑', 'Rel ↓', 'Rel ↑', 'Full ↓', 'Full ↑']
It should be noted that, correctly determined nuclearity means both the bare tree node and its nuclearity are correctly recognized. Correctly determined relation means both the bare node and its relation are correctly recognized, and full means all three aspects are correctly recognized. From the results we can find that, in comparison with the bottom-up approach, the top-down approach can achieve better performance on Bare, Nuc and Rel metrics, while for Full-metric, the performance reduces slightly. Just as noted above, this is due to the difference between the joint learning frameworks behind these two approaches. Among three aspects, the improvement of nuclearity is most, and bare tree structure is weakest. At each level, the performance of these two approaches varies. This suggests that the bidirectional architecture may be an important direction in the future work.