Multi-sentence Argument Linking
Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, Benjamin van Durme . Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020 – 133 citations
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.