# Exploring Span Representations in Neural Coreference Resolution

## Kahardipraja, Patrick and Vyshnevska, Olena and Loáiciga, Sharid

In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.

In Proceedings of the First Workshop on Computational Approaches to Discourse , 2020
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@inproceedings{Kahardipraja-2020,
title = {Exploring Span Representations in Neural Coreference Resolution},
author = {Kahardipraja, Patrick and Vyshnevska, Olena and Lo{\'a}iciga, Sharid},
booktitle = {Proceedings of the First Workshop on Computational Approaches to Discourse},
month = nov,
year = {2020},