Name
Title | ||
|---|---|---|
Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining |
Abstract | ||
|---|---|---|
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT. We further demonstrate that pretraining our parser on the recently available large-scale "silver-standard" discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis. |
Year | Venue | DocType |
|---|---|---|
2020 | COLING | Conference |
Volume | Citations | PageRank |
2020.coling-main | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grigorii Guz | 1 | 0 | 0.68 |
| Patrick Huber | 2 | 0 | 1.69 |
| Giuseppe Carenini | 3 | 2 | 4.41 |