Challenge: Several annotation strategies have been proposed to balance scientific needs with annotation speed.
Approach: They introduce SACR, an easy-to-use coreference chain annotation tool . it is used to annotate large corpora for natural language processing applications . paper compares several annotation schemes implemented in existing tools .
Outcome: The proposed tool is used to annotate large corpora for natural language processing applications.

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ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)

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Challenge: Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications .
Approach: They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP .
Outcome: The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications.
CorefUD 1.0: Coreference Meets Universal Dependencies (2022.lrec-1)

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Challenge: Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotized data.
Approach: They propose a multilingual collection of corpora and a standardized format for coreference resolution compatible with morphosyntactic annotations in the UD framework.
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ParCorFull: a Parallel Corpus Annotated with Full Coreference (L18-1)

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Challenge: Recent research in multilingual coreference and automatic pronoun translation has led to important insights into the problem and some promising results.
Approach: They propose a corpus annotated with full coreference chains that addresses a problem that machine translation and other multilingual natural language processing (NLP) technologies face: translation of coreference across languages.
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ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)

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Challenge: Existing datasets vary in definition of coreferences and are curated for linguistic experts.
Approach: They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets.
Outcome: The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them.
CoRefi: A Crowd Sourcing Suite for Coreference Annotation (2020.emnlp-demos)

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Challenge: Using a web-based coreference annotation suite, we demonstrate that non-expert annotators can be trained to perform and review coreference resolution tasks.
Approach: They propose a web-based coreference annotation suite oriented for crowdsourcing that provides guided onboarding and a novel algorithm for a reviewing phase.
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Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)

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Challenge: Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations.
Approach: They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
Outcome: The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation.
A Probabilistic Annotation Model for Crowdsourcing Coreference (D18-1)

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Challenge: Existing methods to generate annotated corpora for coreference are expensive and limited.
Approach: They propose a model of annotation for aggregating crowdsourced anaphoric annotations.
Outcome: The proposed model can extract from crowdsourced annotations coreference chains comparable to those obtained with expert annotation.
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)

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Challenge: despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved.
Approach: They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved.
Outcome: The proposed model improves the performance of entity coreference resolvers.
SLATE: A Super-Lightweight Annotation Tool for Experts (P19-3)

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Challenge: a new annotation tool is designed to fill the niche of a lightweight interface for terminal users . current tools are built with direct manipulation via a Graphical User Interface (GUI) this approach is time-consuming and difficult to modify .
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Investigating Multilingual Coreference Resolution by Universal Annotations (2023.findings-emnlp)

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Challenge: Existing systems for multilingual coreference resolution have been challenging due to linguistic diversity and complexity of different languages.
Approach: They propose a multilingual coreference dataset with universal morphosyntactic and coreference annotations.
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