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arXiv · 2212.06038

Large Discourse Treebanks from Scalable Distant Supervision

Patrick Huber·Giuseppe Carenini
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Abstract

Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequently downstream tasks) still rely on small-scale human-annotated discourse treebanks, trying to infer general-purpose discourse structures from very limited data in a few narrow domains. To overcome this dire situation and allow discourse parsers to be trained on larger, more diverse and domain-independent datasets, we propose a framework to generate "silver-standard" discourse trees from distant supervision on the auxiliary task of sentiment analysis.

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Patrick Huber, Giuseppe Carenini. 2022-10-18. Large Discourse Treebanks from Scalable Distant Supervision. https://arxiv.org/abs/2212.06038

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