End-to-End Neural Bridging Resolution
Year: 2022
Type: other
Abstract: The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution. In light of these problems, we evaluate bridging resolvers in an end-to-end setting, strengthen them with better encoders, and attempt to gain a better understanding of them via perturbation experiments and a manual analysis of their outputs.
Source: Underline Science Inc.
DOI: 10.48448/dcpr-0w86
Authors: International Committee for Computational Linguistics 2022, Hou, Yufang, Kobayashi, Hideo, Ng, Vincent
Institution: The University of Texas at Dallas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910453967
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