Self-adaptive fine-grained multi-modal data augmentation for semi-supervised muti-modal coreference resolution

Self-adaptive fine-grained multi-modal data augmentation for semi-supervised muti-modal coreference resolution

Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these is...

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Bibliographic Details
Main Authors: ZHENG, Li, CHEN, Boyu, FEI, Hao, LI, Fei, WU, Shengqiong, LIAO, Lizi, JI, Donghong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Coreference Resolution
Multi-modal
Semi-supervised Learning
Artificial Intelligence and Robotics
Computer Sciences
Online Access:https://ink.library.smu.edu.sg/sis_research/9694
https://ink.library.smu.edu.sg/context/sis_research/article/10694/viewcontent/Self_Adaptive_Fine_grain.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9694
https://ink.library.smu.edu.sg/context/sis_research/article/10694/viewcontent/Self_Adaptive_Fine_grain.pdf

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