SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
Table 5: Performance of baseline parsers.
['[EMPTY]', '[BOLD] Dev set  [BOLD] UAS', '[BOLD] Dev set  [BOLD] LAS', '[BOLD] Test set  [BOLD] UAS', '[BOLD] Test set  [BOLD] LAS']
We also measure parsing accuracy with UAS and LAS. The human agreement is presented for comparison. With the addition of tree structural features in relation type prediction, the two-stage dependency parser gets better performance on LAS than vanilla system on both development and test set. Compared with graph-based model, the two transition-based baselines achieve higher accuracy with regard to UAS and LAS. Using more effective training strategies like MIRA may improve graph-based models. We can also see that human performance is still much higher than the three parsers, meaning there is large space for improvement in future work.