COREFERENCE RESOLUTION

AutoRC: Improving BERT Based Relation Classification Models via Architecture Search (2021.acl-srw)

Challenge:	Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities.
Approach:	They propose a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions and a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning.
Outcome:	The proposed model outperforms existing models designed for singular mentions and plural mentions.

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Wei Zhu

Triad-based Neural Network for Coreference Resolution (C18-1)

Challenge:	Existing literature on relation classification models shows no consensus on optimal architecture .
Approach:	They propose a search space for BERT based relation classification models and employ an ENAS method to find better architectures.
Outcome:	The proposed method can find better architectures than baseline models on eight benchmark RC tasks.

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Yuanliang Meng, Anna Rumshisky

Wikipedia as a Resource for Text Analysis and Retrieval (P19-4)

Challenge:	Entity coreference resolution aims to identify mentions that refer to the same entity.
Approach:	They propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution.
Outcome:	The proposed system generates affinity scores between mentions for coreference resolution.

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Marius Pasca

Information Retrieval

Challenge:	Tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval.
Approach:	tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval.
Outcome:	This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval.

An Annotated Dataset of Coreference in English Literature (2020.lrec-1)

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David Bamman, Olivia Lewke, Anya Mansoor

Deep Reinforcement Learning for NLP (P18-5)

Challenge:	Using OntoNotes, coreference resolution systems are typically evaluated on this data exclusively.
Approach:	They present a new dataset of coreference annotations for works of literature in English covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922.
Outcome:	The proposed dataset covers 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922.

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William Yang Wang, Jiwei Li, Xiaodong He

Model-based Annotation of Coreference (2020.lrec-1)

Challenge:	Many natural language processing tasks can be formulated as deep reinforcement learning (DRL) problems.
Approach:	This tutorial provides an introduction to the foundations of deep reinforcement learning . it describes recent advances in designing deep reinforcement for NLP .
Outcome:	This tutorial provides an introduction to the foundations of deep reinforcement learning and some practical solutions for NLP tasks.

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Rahul Aralikatte, Anders Søgaard

BeLeaf: Belief Prediction as Tree Generation (2024.naacl-demo)

Challenge:	Annotators are asked to annotate coreferent spans of text, which is unnatural . we present an alternative in which annotators can preprocess documents and assign pronouns to entities.
Approach:	They propose an alternative in which annotators are asked to assign pronouns to entities and preprocess documents to create a knowledge base.
Outcome:	The proposed model-based approach leads to faster annotation and higher inter-annotator agreement and opens up an alternative approach to coreference resolution.

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John Murzaku, Owen Rambow

Challenge:	a novel approach to predicting source-and-target factuality is presented . our linearized tree generation task fully accounts for the factuity tree structure .
Approach:	They propose a linearized tree generation task which fully accounts for factuality . they then create a system which leverages the linearized representation to create visualizations .
Outcome:	The proposed model and representation fully account for the factuality tree structure, generating the full chain of nested sources instead of the last source only.

French Coreference for Spoken and Written Language (2020.lrec-1)

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Rodrigo Wilkens, Bruno Oberle, Frédéric Landragin, Amalia Todirascu

Z-coref: Thai Coreference and Zero Pronoun Resolution (2024.acl-srw)

Challenge:	In French, most coreference resolution systems run different setups, making comparisons difficult.
Approach:	They present a full-stack model that outperforms other approaches for coreference resolution in French . they compare it with the first end-to-end neural French coreference model trained on democrat .
Outcome:	The proposed model outperforms the current systems for spoken and written French.

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Poomphob Suwannapichat, Sansiri Tarnpradab, Santitham Prom-on

MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020.lrec-1)

Challenge:	limited research and datasets pose significant challenges in Thai language . a proposed model capable of simultaneously handling CR and ZPR tasks takes less time to train .
Approach:	They propose to annotate a Thai-based CR and ZPR dataset and introduce a model that can handle both tasks by adjusting the span definition to include token gaps.
Outcome:	The proposed model outperforms the state-of-the-art in resolving both coreference resolution and zero-pronoun resolution while taking less time to train.

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Scott Martin, Shivani Poddar, Kartikeya Upasani

Natural Language Interfaces

Challenge:	a new dataset, MuDoCo, is composed of authored dialogs between a fictional user and a system . the dialogs cross domains and users exhibit complex task switching behavior .
Approach:	They propose a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system . they propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.
Outcome:	The proposed dataset contains 8,429 dialogs with an average of 5.36 turns per dialog . the users exhibit complex task switching behavior such as re-initiating a previous task .

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (2020.acl-demos)

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Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, Christopher D. Manning

Challenge:	Existing tools that support only a few major languages are under-optimized for accuracy due to a focus on efficiency or use of less powerful models.
Approach:	They introduce a Python natural language processing toolkit that supports 66 languages . they train Stanza on 112 datasets and show it generalizes well on all languages compared to other tools .
Outcome:	The proposed toolkit performs well on 112 datasets and is compatible with the popular Java CoreNLP software.

PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)

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Hong Chen, Zhenhua Fan, Hao Lu, Alan Yuille, Shu Rong

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

Challenge:	Existing methods for coreference resolution are based on word2vec-like representations of entities.
Approach:	They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers .
Outcome:	The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo .

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers (D18-1)

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Nafise Sadat Moosavi, Michael Strube

Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)

Challenge:	Recent coreference resolvers have notable improvements on the CoNLL evaluation sets, but struggle to generalize properly to new datasets.
Approach:	They investigate the role of linguistic features in building more generalizable coreference resolvers . they show that employing features and subsets of their values that are informative for coreference resolution improves generalization .
Outcome:	The proposed system achieves state-of-the-art results on WikiCoref, compared with a system trained on CoNLL.

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Deon Mai, Wei Emma Zhang

LOME: Large Ontology Multilingual Extraction (2021.eacl-demos)

Challenge:	Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones .
Approach:	They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy.
Outcome:	The proposed approach outperforms baseline methods on two benchmark datasets.

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Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme

ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)

Challenge:	LOME is a system for performing multilingual information extraction with large ontologies.
Approach:	They propose a system for multilingual information extraction with a framenet parser . LOME is available as a Docker container on Docker Hub and a lightweight version is available on the web .
Outcome:	The proposed system outperforms or is competitive with the (monolingual) state-of-the-art . it can be used to build knowledge graphs with large ontologies and across multiple languages .

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Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, Brendan O’Connor

CRAFT Shared Tasks 2019 Overview — Integrated Structure, Semantics, and Coreference (D19-57)

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.

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William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter

Understanding Points of Correspondence between Sentences for Abstractive Summarization (2020.acl-srw)

Challenge:	CRAFT corpus provides a unique foundation for integrating natural language processing (NLP) tasks involving structure, semantics, and coreference.
Approach:	They propose to use the CRAFT corpus to evaluate three fundamental language processing tasks over full-text biomedical articles.
Outcome:	The CRAFT corpus provides a unique foundation for integrating natural language processing tasks involving structure, semantics, and coreference.

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Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, Fei Liu

Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)

Challenge:	Using points of correspondence, fusion systems are difficult for abstractive summarizers because of their complexity.
Approach:	They propose to model points of correspondence between disparate sentences by combining documents, source and fusion sentences, and human annotations of points of correspondance between sentences.
Outcome:	The proposed model bridges the gap between coreference resolution and summarization by using human annotations of points of correspondence between sentences.

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Sangha Nam, Minho Lee, Donghwan Kim, Kijong Han, Kuntae Kim, Sooji Yoon, Eun-kyung Kim, Key-Sun Choi

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)

Challenge:	Existing studies on information extraction from unstructured texts lack a coherent evaluation of all tasks.
Approach:	They propose to use crowdsourcing data to develop a Korean information extraction initiative point . they propose to train and evaluate four Korean information extracting tasks using a state-of-the-art model .
Outcome:	The proposed model will be used to evaluate four Korean information extraction tasks using crowdsourcing data.

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Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim

Sentence-Incremental Neural Coreference Resolution (2022.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents .
Approach:	They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems.
Outcome:	The proposed model improves QA performance and retrieval relevance and contextual understanding.

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Matt Grenander, Shay B. Cohen, Mark Steedman

CEREC: A Corpus for Entity Resolution in Email Conversations (2020.coling-main)

Challenge:	Existing systems for identifying spans as mentions are based on mention-pair formulations, but they are not generalized beyond pronouns.
Approach:	They propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method.
Outcome:	The proposed system outperforms state-of-the-art methods on OntoNotes and CODI-CRAC 2021 datasets and is comparable to state- of-the art methods.

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Parag Pravin Dakle, Dan Moldovan

Natural Language Interfaces

Challenge:	e-mail corpus for entity resolution in email conversations is first large scale annotated corpus . ecc is a two-step process with minimal manual effort.
Approach:	They present the first large scale corpus for entity resolution in email conversations . they use 6001 email threads and 38,996 entity coreference chains to construct the corpus .
Outcome:	The proposed corpus is the first large scale annotated corpus for entity resolution in email conversations.

Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations (2022.starsem-1)

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Liyan Xu, Jinho D. Choi

Dialogue Systems & Conversational Agents

Challenge:	Existing models for online decoding on active input are not trained to handle an online decode environment.
Approach:	They propose a new direction of coreference resolution for online decoding on actively generated input such as dialogue . they propose to use models that accept utterances and their past context and find mentions upon each dialogue turn .
Outcome:	The proposed model outperforms the baseline model by 10% on three datasets: Friends, OntoNotes, and BOLT.

Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings (2020.tacl-1)

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Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty

Knowledge Extraction From Texts Based on Wikidata (2022.naacl-industry)

Challenge:	Existing methods for debiasing word embeddings lack gender-based debiases . Existing approaches only reduce gender-related proximity biases by at least 42.02% .
Approach:	They propose a gender debiasing methodology that eliminates bias in word vectors and alters spatial distribution of neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset.
Outcome:	The proposed method outperforms the state-of-the-art in reducing proximity bias by at least 42.02% and reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task.

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Anastasia Shimorina, Johannes Heinecke, Frédéric Herledan

Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective (2023.emnlp-main)

Challenge:	Existing knowledge extraction pipelines for English are not suitable for enterprise use.
Approach:	They propose a knowledge extraction pipeline for English which can be further used for building an entreprise-specific knowledge base.
Outcome:	The proposed pipeline can be used to build an entreprise-specific knowledge base.

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Tianyu Liu, Afra Amini, Mrinmaya Sachan, Ryan Cotterell

KWJA: A Unified Japanese Analyzer Based on Foundation Models (2023.acl-demo)

Challenge:	a new framework for structured prediction is developed for natural language processing . a systematic approach to structured prediction requires exhaustive pair-wise comparisons of tokens .
Approach:	They propose a method that models the relationship between pairs of tokens in a string . they use a parallel method that predicts real numbers for each token in .
Outcome:	The proposed method doubles the speed of graph-based dependency parsers and brings 10-times speed-up over graph-driven dependency parses.

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Nobuhiro Ueda, Kazumasa Omura, Takashi Kodama, Hirokazu Kiyomaru, Yugo Murawaki, Daisuke Kawahara, Sadao Kurohashi

Challenge:	KWJA supports a wide range of tasks including typo correction, word segmentation, word normalization, named entity recognition, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis.
Approach:	They propose to build a Japanese text analyzer based on foundation models that performs a wide range of tasks.
Outcome:	The proposed model performs better in a multi-task manner than other analyzers with specialized models.

Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis (P18-1)

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Tomohide Shibata, Sadao Kurohashi

Deep Neural Networks for Coreference Resolution for Polish (L18-1)

Challenge:	Existing methods for predicate argument structure analysis are difficult and difficult . a Japanese model can detect a zero pronoun and identify a referent of the zero pronominator .
Approach:	They propose a model that performs coreference resolution and predicate argument structure analysis simultaneously.
Outcome:	The proposed model can improve the performance of the inter-sentential zero anaphora resolution drastically.

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Bartłomiej Nitoń, Paweł Morawiecki, Maciej Ogrodniczuk

Improving Automatic Quotation Attribution in Literary Novels (2023.acl-short)

Challenge:	Existing deep neural networks for coreference resolution for Polish have been used to resolve textual fragments that refer to the same entity in the discourse world.
Approach:	They propose a system combining the best deep neural architecture and sieve-based coreference resolvers ordered from most to least precise to achieve the highest results.
Outcome:	The proposed system improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric.

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Krishnapriya Vishnubhotla, Frank Rudzicz, Graeme Hirst, Adam Hammond

Coreference Resolution

Challenge:	Existing methods for quotation attribution in literary novels require varying levels of available information.
Approach:	They propose to train and evaluate models for character identification, coreference resolution, quotation identification and speaker attribution tasks using an annotated dataset.
Outcome:	The proposed model scores on speaker attribution task on the same scale as state-of-the-art models.

End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)

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Hongliang Fei, Xu Li, Dingcheng Li, Ping Li

IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark for LLMs (2025.naacl-short)

Challenge:	Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions.
Approach:	They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics.
Outcome:	The proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.

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Kawshik Manikantan, Makarand Tapaswi, Vineet Gandhi, Shubham Toshniwal

Challenge:	Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models’ referential understanding.
Approach:	They propose a benchmark for mention resolution presented in a multiple-choice question format and a curated mixture of different mention types and corresponding entities.
Outcome:	The proposed model achieves 81.9% accuracy while the open model achieve 80%.

Coreference Resolution with Entity Equalization (P19-1)

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Ben Kantor, Amir Globerson

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	Existing approaches to coreference resolution capture the properties of entity clusters and use them in the resolution process.
Approach:	They propose an approach that captures entities and uses them in coreference resolution . they propose an "Entity Equalization" mechanism that represents each mention in a cluster .
Outcome:	The proposed approach improves the CoNLL-2012 coreference resolution task by 3.6%.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

Evaluating and Improving the Coreference Capabilities of Machine Translation Models (2023.eacl-main)

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

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Asaf Yehudai, Arie Cattan, Omri Abend, Gabriel Stanovsky

Autoregressive Structured Prediction with Language Models (2022.findings-emnlp)

Challenge:	Currently, end-to-end models learn coreference resolution implicitly by observing aligned sentences in bilingual corpora.
Approach:	They develop a method that derives coreference clusters from MT output and evaluates them without requiring annotations in the target language.
Outcome:	The proposed model outperforms existing models on three challenging benchmarks.

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Tianyu Liu, Yuchen Eleanor Jiang, Nicholas Monath, Ryan Cotterell, Mrinmaya Sachan

Crowdsourcing and Aggregating Nested Markable Annotations (P19-1)

Challenge:	Recent years have seen a paradigm shift in NLP towards using pretrained language models for a wide range of tasks.
Approach:	They propose to model structures as sequences of actions in autoregressive manner with PLMs . their approach allows in-structure dependencies to be learned without any loss .
Outcome:	The proposed approach achieves state-of-the-art on all structured prediction tasks.

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Chris Madge, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Silviu Paun, Massimo Poesio

Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets (2021.acl-long)

Challenge:	Existing methods for identifying markables for coreference annotation are task and language-independent and can be used for a variety of other annotation tasks.
Approach:	They propose a method for identifying markables for coreference annotation that combines automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model.
Outcome:	The proposed method improves mention boundaries on news and other genres by over seven percentage points compared with state-of-the-art, domain-independent automatic mention detectors and almost three points over an in-domain mention detector.

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Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, Hanna Wallach

RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context (2025.acl-srw)

Challenge:	Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms.
Approach:	They apply a measurement modeling lens to inventory pitfalls that threaten benchmarks' validity as measurement models for stereotyping.
Outcome:	The proposed benchmarks lack clarity and assumptions that affect how they conceptualize and operationalize stereotyping.

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Andrey Chirkin, Svetlana Kuznetsova, Maria Volina, Anna Dengina

Challenge:	a new context understanding benchmark is proposed for short-context understanding in Russian . the benchmarks focus on broad reasoning tasks or long-concept comprehension, but are limited in their ability to perceive subtle nuances of context.
Approach:	They propose a new benchmark for evaluating short-context understanding in Russian . they propose to use four tasks to assess model performance from a specific perspective .
Outcome:	The proposed benchmark is adapted to Russian-language data.

Longtonotes: OntoNotes with Longer Coreference Chains (2023.findings-eacl)

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Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan

Higher-Order Coreference Resolution with Coarse-to-Fine Inference (N18-2)

Challenge:	Using Ontonotes, documents in certain genres were split into smaller parts for ease of annotation.
Approach:	They propose to merge annotations from documents split into smaller parts in Ontonotes for ease of annotation.
Outcome:	The proposed corpus restores documents to their original form, revealing dramatic increases in length in certain genres.

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Kenton Lee, Luheng He, Luke Zettlemoyer

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)

Challenge:	a new approach to coreference resolution uses a span-ranking architecture as an attention mechanism to iteratively refine span representations.
Approach:	They propose a fully-differentiable approximation to higher-order inference for coreference resolution . they propose introducing a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor .
Outcome:	The proposed model significantly improves accuracy on the English OntoNotes benchmark while being far more computationally efficient.

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Qiang Gao, Zixiang Meng, Bobo Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)

Challenge:	Document-level event extraction aims to extract structured information from unstructured text.
Approach:	They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events.
Outcome:	The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

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Ulrich Rückert, Srinivas Sunkara, Abhinav Rastogi, Sushant Prakash, Pranav Khaitan

Dialogue Systems & Conversational Agents

Challenge:	Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships .
Approach:	They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context.
Outcome:	The proposed model is based on a large human-human conversation corpus with people and location annotations.

Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)

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Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven R. Corman

Question Answering

Challenge:	Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention.
Approach:	They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have.
Outcome:	The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger.

Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)

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Yujian Gan, Massimo Poesio, Juntao Yu

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)

Challenge:	Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood.
Approach:	They propose that future efforts should improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs.
Outcome:	The proposed methods improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs.

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Severine Verlinden, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder

A Structured Span Selector (2022.naacl-main)

Challenge:	Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together.
Approach:	They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking.
Outcome:	The proposed model improves on two datasets with 5% F1 score.

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Tianyu Liu, Yuchen Jiang, Ryan Cotterell, Mrinmaya Sachan

It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance (2023.findings-acl)

Challenge:	a typical approach to natural language processing tasks involves selecting text spans and making decisions about them.
Approach:	They propose a grammar-based structured span selection model which learns to make use of partial span annotations.
Outcome:	The proposed model improves on two popular span prediction tasks.

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Arjun Subramonian, Xingdi Yuan, Hal Daumé III, Su Lin Blodgett

Challenge:	a meta-analysis and survey of practitioners reveal that benchmarks suffer from operationalization disagreements.
Approach:	They propose a taxonomy of disagreement to explain disagreements in NLP benchmarks . they propose defining how tasks are conceptualized and operationalizing benchmarks to document their limitations.
Outcome:	The proposed taxonomy identifies two types of disagreements among NLP practitioners . it shows that benchmarks are not clearly conceptualized and suffer from operationalization disagreements .

Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)

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Shahar Levy, Koren Lazar, Gabriel Stanovsky

Graph Refinement for Coreference Resolution (2022.findings-acl)

Challenge:	Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets.
Approach:	They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments.
Outcome:	The proposed method extends the existing dataset to 108K diverse English sentences.

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Lesly Miculicich, James Henderson

BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels (D19-1)

Challenge:	Existing models for coreference resolution are based on independent mention pair-wise decisions.
Approach:	They propose a model that learns coreference at the document-level and takes global decisions.
Outcome:	The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution.

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Yimin Jing, Deyi Xiong, Zhen Yan

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Using BiPaR, we build monolingual, multilingual and cross-lingual MRC on novels.
Approach:	They propose a bilingual parallel novel-style machine reading comprehension dataset BiPaR . they collect 3,667 bilingual parallel paragraphs from Chinese and English novels .
Outcome:	The proposed dataset supports multilingual and cross-lingual reading comprehension.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution (2021.naacl-main)

Challenge:	Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain.
Approach:	They propose a pre-training method to improve the joint extraction performance with just extra entity annotations.
Outcome:	The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks.

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Tuan Lai, Heng Ji, Trung Bui, Quan Hung Tran, Franck Dernoncourt, Walter Chang

Detecting Scenes in Fiction: A new Segmentation Task (2021.eacl-main)

Challenge:	Existing methods for event coreference resolution use symbolic features, but they are noisy and contain errors.
Approach:	They propose a context-dependent gated module to adaptively control the information flows from the input symbolic features.
Outcome:	The proposed model achieves state-of-the-art on two datasets: ACE 2005 and KBP 2016 .

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Albin Zehe, Leonard Konle, Lea Katharina Dümpelmann, Evelyn Gius, Andreas Hotho, Fotis Jannidis, Lucas Kaufmann, Markus Krug, Frank Puppe, Nils Reiter, Annekea Schreiber, Nathalie Wiedmer

Challenge:	Text segmentation is a long standing issue in the area of natural language processing . even modern methods struggle with processing text longer than a couple of sentences or paragraphs .
Approach:	They introduce the task of scene segmentation on narrative texts and provide an annotated corpus . they discuss linguistic and narrative properties of the task and provide baseline experiments .
Outcome:	The proposed task is very challenging and the results are impressive.

On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning (2021.naacl-main)

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Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, Xiang Ren

GenderQuant: Quantifying Mention-Level Genderedness (N19-1)

Challenge:	PTLMs can exhibit biases against protected groups in a host of modeling tasks . but, fine-tuned LMs may propagate bias to downstream classifiers .
Approach:	They propose to use upstream bias mitigation techniques to reduce bias on downstream tasks by fine-tuning an upstream model and applying it to a downstream model.
Outcome:	The proposed model reduces bias on hate speech detection, toxicity detection and coreference resolution tasks over bias factors.

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null Ananya, Nitya Parthasarthi, Sameer Singh

Ethical NLP

Challenge:	Existing approaches to detect gendered language require considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features.
Approach:	They use existing NLP pipelines to automatically annotate gender of mentions in the text and train a supervised classifier to predict the gender of any mention from its context and evaluate it on unseen text.
Outcome:	The proposed method can detect gendered language on movie summaries, movie reviews, news articles, and fiction novels.

He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues (2022.findings-emnlp)

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Amanda Bertsch, Graham Neubig, Matthew R. Gormley

Sequential Cross-Document Coreference Resolution (2021.emnlp-main)

Challenge:	Existing work on style transfer has focused on controlling formality, authorial style, and sentiment of text.
Approach:	They propose a style transfer task that reframes a dialogue from informal first person to formal third person rephrasing . they use a dataset to annotate dialogues from a text summarization corpus .
Outcome:	The proposed task improves the performance of extractive models on a dialogue summarization dataset.

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Emily Allaway, Shuai Wang, Miguel Ballesteros

The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (P19-1)

Challenge:	Existing models for cross-document coreference resolution have been used for within-document entity coreference but have been relatively limited.
Approach:	They propose a model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference.
Outcome:	The proposed model achieves competitive results for entity and event coreference while minimizing error propagation in complex reasoning tasks.

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Ali Emami, Paul Trichelair, Adam Trischler, Kaheer Suleman, Hannes Schulz, Jackie Chi Kit Cheung

Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022.naacl-main)

Challenge:	Existing methods for coreference resolution exploit the number and gender of antecedents or have been handcrafted and do not reflect the diversity of naturally occurring text.
Approach:	They propose a trick to improve resolution by antecedent switching to target common-sense understanding and world knowledge.
Outcome:	The proposed method achieves state-of-the-art results on the GAP coreference task.

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Liyan Xu, Jinho Choi

Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection (P19-1)

Challenge:	Existing work on document-level relation extraction has focused on end-to-end setting that extracts global entities and relations jointly.
Approach:	They propose to introduce a two-way interaction between COREF and RE that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference.
Outcome:	The proposed model achieves the best performance by up to 2.3/5.1 F1 over the baseline.

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Nafise Sadat Moosavi, Leo Born, Massimo Poesio, Michael Strube

Moving on from OntoNotes: Coreference Resolution Model Transfer (2021.emnlp-main)

Challenge:	Existing methods to evaluate maximum spans tangle coreference evaluation with mention boundary detection . however, this method is costly and does not scale to large corpora.
Approach:	They propose an algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora.
Outcome:	The proposed algorithm is consistent with those manually annotated by experts.

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Patrick Xia, Benjamin Van Durme

Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020.acl-main)

Challenge:	Academic neural models for coreference resolution (coref) are typically trained on OntoNotes and model improvements are benchmarked on that dataset.
Approach:	They aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset.
Outcome:	The proposed model improvements are consistent with the state-of-the-art results on PreCo.

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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)

Challenge:	Unsupervised pretraining has recently pushed the state of the art on many natural language understanding tasks.
Approach:	They perform a large-scale survey on a pretrained RoBERTa model with 110 intermediate-target task combinations and 25 probing tasks to reveal the specific skills that drive transfer.
Outcome:	The proposed model is trained on 110 intermediate-target task combinations and compared with 25 probing tasks to reveal the specific skills that drive transfer.

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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai

Low-Resource NLP

Challenge:	Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training.
Approach:	They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks.
Outcome:	The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning.

Adapting Coreference Resolution Models through Active Learning (2022.acl-long)

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Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, Jordan Boyd-Graber

CorefUD 1.0: Coreference Meets Universal Dependencies (2022.lrec-1)

Challenge:	Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains.
Approach:	They investigate how to actively label coreference by sampling a small subset of data for annotators to label.
Outcome:	The proposed model can be more realistic when labeling spans within the same document than when annotating spans across documents.

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Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský, Amir Zeldes, Daniel Zeman

Disambiguating Reference in Visually Grounded Dialogues through Joint Modeling of Textual and Multimodal Semantic Structures (2025.acl-long)

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.
Outcome:	The proposed framework is compatible with morphosyntactic annotations and includes facilities for related tasks such as named entity recognition.

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Shun Inadumi, Nobuhiro Ueda, Koichiro Yoshino

Visual Data in NLP

Challenge:	incorporating textual and multimodal reference resolution improves performance in visual-based reference resolution . Phrase grounding is a well-established task for understanding semantic relations between mentions and objects . ambiguities caused by pronouns and ellipses can arise in visually grounded dialogues .
Approach:	They propose a framework that unifies textual and multimodal reference resolution by mapping mention embeddings to object embeddements and selecting mentions or objects based on their similarity.
Outcome:	The proposed framework performs better in phrase grounding than other models for this task.

The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction (2025.coling-main)

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Arthur Amalvy, Vincent Labatut, Richard Dufour

BERT for Coreference Resolution: Baselines and Analysis (D19-1)

Challenge:	low-level tasks are used to extract character networks from literary texts, but no study has been conducted on their impact on performance.
Approach:	They focus on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks.
Outcome:	The proposed methods outperform traditional pipelines in terms of recall and recall.

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Mandar Joshi, Omer Levy, Luke Zettlemoyer, Daniel Weld

Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)

Challenge:	Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition.
Approach:	They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks.
Outcome:	A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing.

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Nupoor Gandhi, Anjalie Field, Emma Strubell

Word-Level Coreference Resolution (2021.emnlp-main)

Challenge:	Recent results show that annotating mentions is twice as fast as annotation of full coreference chains.
Approach:	They propose a method for efficiently adapting coreference models using only mentions in the target domain without increasing annotator time.
Outcome:	The proposed method improves average F1 without increasing annotator time.

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Vladimir Dobrovolskii

Persian Discourse Treebank and coreference corpus (L18-1)

Challenge:	Recent coreference resolution models rely heavily on span representations to find coreference links between word spans.
Approach:	They propose to consider coreference links between individual words rather than word spans and reconstruct the word span.
Outcome:	The proposed model outperforms existing models on the OntoNotes benchmark while being highly efficient.

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Azadeh Mirzaei, Pegah Safari

Syntactic Text Processing

Challenge:	Currently, we are adding a new document-level discourse annotation to our new corpus.
Approach:	They propose to build a Persian discourse treebank and a comprehensive Persian coreference corpus based on discourse analysis and coreference resolution.
Outcome:	The proposed corpus includes 30000 individual sentences with morphological, syntactic and semantic labels and nearly half a million tokens.

AMALGUM – A Free, Balanced, Multilayer English Web Corpus (2020.lrec-1)

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Luke Gessler, Siyao Peng, Yang Liu, Yilun Zhu, Shabnam Behzad, Amir Zeldes

Challenge:	a corpus of 4M tokens is available online with a large number of high-quality annotation layers.
Approach:	They propose to use a genre-balanced English web corpus with multiple annotation layers . they harness knowledge from multiple annotation layer to achieve a "better than NLP" benchmark .
Outcome:	The proposed corpus is genre-balanced and features high-quality automatic annotation layers.

Character Coreference Resolution in Movie Screenplays (2023.findings-acl)

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Sabyasachee Baruah, Shrikanth Narayanan

Robustness in NLP

Challenge:	Movie screenplays have a distinct narrative structure.
Approach:	They develop a method to extract structural information and character coreference clusters from movie screenplays by leveraging a movie parser and a character coreferser.
Outcome:	The proposed methods scale to long movie screenplays without dramatically increasing their memory footprints.

A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)

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Hongjie Cai, Heqing Ma, Jianfei Yu, Rui Xia

Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution (2021.emnlp-main)

Challenge:	Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities.
Approach:	They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT.
Outcome:	The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets.

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Denis Emelin, Rico Sennrich

Semi-supervised multimodal coreference resolution in image narrations (2023.emnlp-main)

Challenge:	Winograd schemas are well-established tools for evaluating coreference resolution and commonsense reasoning capabilities of computational models.
Approach:	They present a dataset of German, French, and Russian schemas aligned with their English counterparts.
Outcome:	The proposed model improves in English and German, while the model improve in other languages.

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Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen

Revealing the Myth of Higher-Order Inference in Coreference Resolution (2020.emnlp-main)

Challenge:	a semi-supervised approach is used to resolve multimodal coreferences and narrative grounding in a multimodal context.
Approach:	They propose a semi-supervised approach that utilizes image-narration pairs to resolve coreferences and narrative grounding in a multimodal context.
Outcome:	The proposed approach outperforms baselines quantitatively and qualitatively for coreference resolution and narrative grounding in a multimodal context.

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Liyan Xu, Jinho D. Choi

Pre-training Mention Representations in Coreference Models (2020.emnlp-main)

Challenge:	Adapted coreference resolution models have only marginally improved performance over representation learning.
Approach:	They implement an end-to-end coreference system and four HOI approaches to analyze the impact of higher-order inference on coreference resolution.
Outcome:	The proposed model shows that the impact of higher-order inference (HOI) on coreference resolution is negative to marginal, providing a new perspective on the task.

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Yuval Varkel, Amir Globerson

Seq2seq is All You Need for Coreference Resolution (2023.emnlp-main)

Challenge:	Existing methods to improve coreference resolution use labeled data.
Approach:	They propose two self-supervised tasks that are closely related to coreference resolution to improve mention representation.
Outcome:	The proposed models improve mention representations by learning them on a GAP dataset.

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Wenzheng Zhang, Sam Wiseman, Karl Stratos

Multi-Sentence Argument Linking (2020.acl-main)

Challenge:	Existing work on coreference resolution suggests task-specific models are necessary . a recent line of work that take an alternative approach leveraging advances in seq2seq-based models is needed .
Approach:	They propose a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation.
Outcome:	The proposed model outperforms or matches the best coreference systems on an array of datasets.

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Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, Benjamin Van Durme

Challenge:	Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking.
Approach:	They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution.
Outcome:	The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types.

Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)

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Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty

Data-driven Coreference-based Ontology Building (2024.findings-emnlp)

Challenge:	integrating coreference and decomposition increases recall on rare relations by over 20%.
Approach:	They propose an open-source pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition.
Outcome:	The proposed pipeline achieves a 99.8% exact-match accuracy on sentence simplification.

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Shir Ashury Tahan, Amir Cohen, Nadav Cohen, Yoram Louzoun, Yoav Goldberg

COMET-M: Reasoning about Multiple Events in Complex Sentences (2023.findings-emnlp)

Challenge:	a new ontology is based on coreference resolution, but it is not comprehensive . a recent study found that ontologies categorize concepts into groups and arrange them in a hierarchy .
Approach:	They derive coreference chains from a corpus of 30 million biomedical abstracts and construct a graph based on the string phrases within these chains.
Outcome:	The proposed ontology overlaps significantly with human-authored ontologies.

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Sahithya Ravi, Raymond Ng, Vered Shwartz

Reasoning

Challenge:	Existing commonsense models that generate event-centric inferences for simple sentences struggle with the complexity of multi-event sentences prevalent in natural text.
Approach:	They propose a commonsense model that generates inferences for a target event within a complex sentence using a multi-event inference dataset.
Outcome:	The proposed model produces inferences for a target event within a complex sentence taking the complete context into account.

Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)

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Yujian Gan, Yuan Liang, Jinxia Xie, Yanni Lin, Juntao Yu, Massimo Poesio

Multilingual Coreference Resolution in Low-resource South Asian Languages (2024.lrec-main)

Challenge:	Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations.
Approach:	They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution .
Outcome:	The proposed approach achieves better performance on the English OntoNotes development set.

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Ritwik Mishra, Pooja Desur, Rajiv Ratn Shah, Ponnurangam Kumaraguru

Un-considering Contextual Information: Assessing LLMs’ Understanding of Indexical Elements (2025.findings-acl)

Challenge:	Existing coreference resolution models for South Asian languages are limited . a a sanity check for the prediction of translations is required to ensure accuracy of the model, authors say .
Approach:	They evaluate an end-to-end coreference resolution model on a Hindi golden set . they use translation and word-alignment tools to translate a translated dataset into 31 languages .
Outcome:	The proposed model scored 64 and 68 on a Hindi golden set.

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Metehan Oğuz, Yavuz Faruk Bakman, Duygu Nur Yaldiz

Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) excel in coreference resolution tasks, but previous studies only assessed performance with nouns and third person pronouns.
Approach:	They evaluate LLMs' performance on coreference resolution with indexicals like I, you, here and tomorrow which come with unique challenges due to their linguistic properties.
Outcome:	The proposed models perform well with some indexicals while struggling with others.

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Amber Shore, Russell Scheinberg, Ameeta Agrawal, So Young Lee

Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)

Challenge:	Large Language Models (LLMs) are intended to reflect human linguistic competencies . but when context is absent or insufficient, ambiguity resolution becomes more tenuous .
Approach:	They propose a CORRECT-DETECT trade-off between large language models and ambiguity detection . they show that large language model models can achieve good performance with minimal prompting .
Outcome:	The proposed models can achieve good performance with minimal prompting in coreference disambiguation and detection of ambiguity in corefertility tasks, but they cannot do both at the same time.

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Bruno Gatti, Giuliano Martinelli, Roberto Navigli