18,149 research outputs found
RST-style Discourse Parsing Guided by Document-level Content Structures
Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how
clauses, sentences, and large text spans compose a whole discourse and presents
the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines
construct rhetorical structures without the knowledge of document-level content
structures, which causes relatively low performance when predicting the
discourse relations for large text spans. Recognizing the value of high-level
content-related information in facilitating discourse relation recognition, we
propose a novel pipeline for RST-DP that incorporates structure-aware news
content sentence representations derived from the task of News Discourse
Profiling. By incorporating only a few additional layers, this enhanced
pipeline exhibits promising performance across various RST parsing metrics
Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsing
Complex feature extractors are widely employed for text representation
building. However, these complex feature extractors make the NLP systems prone
to overfitting especially when the downstream training datasets are relatively
small, which is the case for several discourse parsing tasks. Thus, we propose
an alternative lightweight neural architecture that removes multiple complex
feature extractors and only utilizes learnable self-attention modules to
indirectly exploit pretrained neural language models, in order to maximally
preserve the generalizability of pre-trained language models. Experiments on
three common discourse parsing tasks show that powered by recent pretrained
language models, the lightweight architecture consisting of only two
self-attention layers obtains much better generalizability and robustness.
Meanwhile, it achieves comparable or even better system performance with fewer
learnable parameters and less processing time
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