Predicting Discourse Structure using Distant Supervision from Sentiment
Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined
['Approach', 'RST-DTtest', 'Instr-DTtest']
We further analyze our findings with respect to baselines and existing discourse parsers. The first set of results in Table 3 shows that the hierarchical right/left branching baselines dominate the completely right/left branching ones. However, their performance is still significantly worse than any discourse parser (intra- and interdomain). [CONTINUE] The second set of results show the performance of existing discourse parsers when trained and tested on the same dataset (intra-domain). [CONTINUE] The Two-Stage approach by Wang et al. (2017) achieves the best performance with 86% on the structure prediction using the RST-DT dataset. On the Instructional dataset, the CODRA discourse parser by Joty et al. (2015) achieves the highest score with 82.88%. [CONTINUE] The third set in the table shows the key results from Phase 1 on the inter-domain performance. [CONTINUE] While the avg attention-aggregation function achieves the most consistent performance the max function on both evaluation corpora, should not be dismissed, as it performs better on the larger RST-DT dataset, which is arguably more related to the Yelp'13 corpus than the sentimentneutral Instr-DT.