Coreference Resolution
261 papers with code • 16 benchmarks • 43 datasets
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
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I voted for Obama because he was most aligned with my values", she said.
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"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
Benchmarks
These leaderboards are used to track progress in Coreference Resolution
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Libraries
Use these libraries to find Coreference Resolution models and implementationsDatasets
WSC
OntoNotes 5.0
WinoBias
GAP Coreference Dataset
Quoref
Latest papers with no code
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.
Gender-specific Machine Translation with Large Language Models
While machine translation (MT) systems have seen significant improvements, it is still common for translations to reflect societal biases, such as gender bias.
Generalised Winograd Schema and its Contextuality
In this work, we focus on coreference ambiguities and investigate the Winograd Schema Challenge (WSC), a test proposed by Levesque in 2011 to evaluate the intelligence of machines.
PronounFlow: A Hybrid Approach for Calibrating Pronouns in Sentences
Flip through any book or listen to any song lyrics, and you will come across pronouns that, in certain cases, can hinder meaning comprehension, especially for machines.
DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference.
Athena 2.0: Discourse and User Modeling in Open Domain Dialogue
Conversational agents are consistently growing in popularity and many people interact with them every day.
Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models
Customer feedback is invaluable to companies as they refine their products.
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation
SimpleMTOD is a simple language model which recasts several sub-tasks in multimodal task-oriented dialogues as sequence prediction tasks.
Improving Automatic Quotation Attribution in Literary Novels
Current models for quotation attribution in literary novels assume varying levels of available information in their training and test data, which poses a challenge for in-the-wild inference.
Examining risks of racial biases in NLP tools for child protective services
Given well-established racial bias in this setting, we investigate possible ways deployed NLP is liable to increase racial disparities.
