# Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

arXiv: https://arxiv.org/abs/2104.10215 · HTML: https://mlsanity.com/paper/2104.10215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Using Type Information to Improve Entity Coreference Resolution](https://mlsanity.com/paper/2010.05738/) (2020)

## Built on by (most cited first, 2 of 2)

- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://mlsanity.com/paper/2409.14969/) (2024)
- [Light Coreference Resolution for Russian with Hierarchical Discourse Features](https://mlsanity.com/paper/2306.01465/) (2023)

## Abstract (arXiv)

Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.