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arXiv · 1911.03766

Multi-Sentence Argument Linking

Seth Ebner·Patrick Xia·Ryan Culkin·Kyle Rawlins·Benjamin Van Durme
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Abstract

We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.

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Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, Benjamin Van Durme. 2020-05-09. Multi-Sentence Argument Linking. https://arxiv.org/abs/1911.03766

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