A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01.
['[BOLD] Approach', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
Using encoder hidden states as decoder inputs and adopting dot product as the attention score function together gives 0.40%-7.29% relative improvement in F1 over the first four baselines. Using ELMo, our segmenter outperforms all the baselines in all three measures. We achieve 2.3%-11.9%, 2.4%-11.3% and 2.3%-12.3% relative improvements in F1, Recall and Precision, respectively. Jointly training with the parser improves this further (95.55 F1). It is worthwhile to mention that our segmenter’s performance of 95.55 F1 is very close to the human agreement of 98.3 F1. ELMo, as a transfer learning method, provides notable improvements. A similar observation was reported in Wang et al. Surprisingly, the results with BERT were not as good. We suspect this is due to BERT’s special tokenization.