Antecedent Prediction Without a Pipeline
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CitationWiseman, Sam, Alexander M. Rush, and Stuart Shieber. 2016. Antecedent Prediction Without a Pipeline. In Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016), San Diego, CA, June 16, 2016: 53-58.
AbstractWe consider several antecedent prediction models that use no pipelined features generated by upstream systems. Models trained in this way are interesting because they allow for side-stepping the intricacies of upstream models, and because we might expect them to generalize better to situations in which upstream features are unavailable or unreliable. Through quantitative and qualitative error analysis we identify what sorts of cases are particularly difficult for such models, and suggest some directions for further improvement.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29347339
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