Challenge: Existing studies on coreference resolution for Twitter texts show that performance is low.
Approach: They propose to use Twitter conversations to train a system that is originally trained on OntoNotes to improve coreference resolution.
Outcome: The proposed system outperforms existing systems on Twitter by 21.6%.

Similar Papers

Variation in Coreference Strategies across Genres and Production Media (2020.coling-main)

Copied to clipboard

Challenge: a lack of work on automatic coreference resolution on spoken and written language has led to inconclusive results.
Approach: They propose to use Ontonotes, Switchboard and Twitter to investigate coreference . they find fairly clear patterns of "behavior" for the different genres/medias .
Outcome: The results show that the choice of genre and the medium (spoken versus spoken) relates to the spokenwritten spectrum for coreference strategies.
Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)

Copied to clipboard

Challenge: Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference.
Approach: They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly.
Outcome: The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese.
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance (2021.naacl-main)

Copied to clipboard

Challenge: Recent work on entity coreference resolution (CR) follows current trends in Deep Learning . traditional approaches do not make use of hierarchical representations of discourse structure .
Approach: They propose to leverage automatically constructed discourse parse trees within a neural approach to generate anaphoric mentions.
Outcome: The proposed model improves on two benchmark entity coreference-resolution datasets.
xCoRe: Cross-context Coreference Resolution (2025.emnlp-main)

Copied to clipboard

Challenge: Current coreference resolution systems are limited to short-to-medium-sized documents and struggle to scale to very long documents due to architectural limitations and implied memory costs.
Approach: They propose a unified approach to coreference resolution that unifies two challenging settings . they use a pipeline that first identifies mentions, then creates clusters within individual contexts .
Outcome: The proposed model achieves state-of-the-art results on cross-document benchmarks and strong performance on long-document data while retaining top-tier results on traditional datasets.
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)

Copied to clipboard

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.
OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres (2021.acl-short)

Copied to clipboard

Challenge: Existing methods for coreference resolution are unable to evaluate generalizability to open domain data.
Approach: They propose to make an OntoNotes-like coreference dataset publicly available and convert it into an English corpus.
Outcome: The proposed dataset is the largest human-annotated coreference corpus following the OntoNotes guidelines and the first to be evaluated for consistency with the OnToNote's scheme.
On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks (2020.lrec-1)

Copied to clipboard

Challenge: Existing word embeddings rely on local information delimited by context windows or dependency parents to predict word relations.
Approach: They propose to use coreference resolution to find all spans of a text that refer to the same entity to improve the F1-Scores.
Outcome: The proposed methods do not benefit significantly from pronoun substitution.
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)

Copied to clipboard

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.
Toward Gender-Inclusive Coreference Resolution (2020.acl-main)

Copied to clipboard

Challenge: a recent study shows that coreference resolution systems can be harmful to binary and non-binary trans and cis stakeholders.
Approach: They propose to use gender-based crowd annotations to investigate coreference resolution biases . they use a dataset to examine the complexity of gender in crowd annotation systems .
Outcome: a new study shows that without acknowledging and building systems that recognize gender, we build systems that lead to many potential harms.
Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021.eacl-main)

Copied to clipboard

Challenge: Character linking is the task of linking mentioned people in conversations to the real world . human use of pronouns or normal entities makes it difficult to link mentioned people to real people . a critical step towards understanding conversations is grounding mentioned people - a goal of the natural language processing community .
Approach: They propose to integrate richer context from the coreference relations among different mentions to help the linking task.
Outcome: The proposed model outperforms all previous models on both tasks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations