Challenge: Multimodal coreference resolution (MCR) aims to identify mentions referring to the same entity across different modalities, such as text and visuals.
Approach: They propose a Chinese multimodal coreference dataset based on Douyin short-video platform to help researchers understand multimodal content.
Outcome: The proposed dataset pairs short videos with corresponding textual dialogues from user comments and includes manually annotated coreference clusters for person mentions in the text and the coreferential person head regions in the corresponding video frames.

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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.
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Outcome: The proposed dataset improves the baseline system by 0.9% . the proposed dataset is based on the framework of Universal Dependencies 2 .
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)

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Challenge: Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents.
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Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)

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Challenge: Existing datasets for entity coreference resolution are limited to English and other languages are rare.
Approach: They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi.
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MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)

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Challenge: Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English.
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A Large-Scale Chinese Multimodal NER Dataset with Speech Clues (2021.acl-long)

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Challenge: Using a large-scale dataset, we explore Chinese named entity recognition (NER) with both textual and acoustic contents.
Approach: They propose a Chinese multimodal named entity recognition dataset . their corpus contains 42,987 annotated sentences and 71 hours of speech data .
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Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021.eacl-main)

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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.
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Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles (2024.lrec-main)

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Challenge: Existing methods for cross-document coreference resolution do not provide images for all mentions of events.
Approach: They propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models.
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Disambiguating Reference in Visually Grounded Dialogues through Joint Modeling of Textual and Multimodal Semantic Structures (2025.acl-long)

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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 .
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NovelCR: A Large-Scale Bilingual Dataset Tailored for Long-Span Coreference Resolution (2025.findings-acl)

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Challenge: Existing coreference resolution datasets are either small in scale or restrict coreference to a limited text span.
Approach: They present a large-scale bilingual benchmark for long-span coreference resolution . they find that NovelCR is notably rich in long-spanning coreference pairs .
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Semi-supervised multimodal coreference resolution in image narrations (2023.emnlp-main)

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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.
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