Coreference for Discourse Parsing: A Neural Approach

Grigorii Guz, Giuseppe Carenini
2020 Proceedings of the First Workshop on Computational Approaches to Discourse   unpublished
We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution
more » ... erence resolution are jointly learned in a neural multitask fashion. Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.
doi:10.18653/v1/2020.codi-1.17 fatcat:4bcw2pag65fmfpuupiqy62j6jq