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Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Self-paced learning for latent variable models
M. Pawan Kumar, Benjamin Packer, and Daphne Koller. 2010 · 2010
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Sentiment Analysis and Opinion Mining
Bing Liu. 2012 · 2012
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Semeval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
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Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. 2015 · 2015
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Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective
Tieliang Gong, Qian Zhao, Deyu Meng, and Zongben Xu. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Multimodal named entity recognition for short social media posts
Seungwhan Moon, Leonardo Neves, and Vitor Carvalho. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Open-domain targeted sentiment analysis via span-based extraction and classification
Minghao Hu, Yuxing Peng, Zhen Huang, Dongsheng Li, and Yiwei Lv. 2019 · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
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Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, and Tom M. Mitchell. 2019 · 2019
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Dynamically composing domain-data selection with clean-data selection by "co-curricular learning" for neural machine translation
Wei Wang, Isaac Caswell, and Ciprian Chelba. 2019 · 2019
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Multi-interactive memory network for aspect based multimodal sentiment analysis
Nan Xu, Wenji Mao, and Guandan Chen. 2019 · 2019
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Adapting bert for target-oriented multimodal sentiment classification
Jianfei Yu and Jing Jiang. 2019 · 2019
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Joint aspect extraction and sentiment analysis with directional graph convolutional networks
Guimin Chen, Yuanhe Tian, and Yan Song. 2020 · 2020
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Joint multi-modal aspect-sentiment analysis with auxiliary cross-modal relation detection
Xincheng Ju, Dong Zhang, Rong Xiao, Junhui Li, Shoushan Li, Min Zhang, and Guodong Zhou. 2021 · 2021
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Exploiting BERT for multimodal target sentiment classification through input space translation
Zaid Khan and Yun Fu. 2021 · 2021
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Exploiting curriculum learning in unsupervised neural machine translation
Jinliang Lu and Jiajun Zhang. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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Rpbert: A text-image relation propagation-based BERT model for multimodal NER
Lin Sun, Jiquan Wang, Kai Zhang, Yindu Su, and Fangsheng Weng. 2021 · 2021
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Norm-based curriculum learning for neural machine translation
Xuebo Liu, Houtim Lai, Derek F. Wong, and Lidia S. Chao. 2020 · 2020
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RIVA: A pre-trained tweet multimodal model based on text-image relation for multimodal NER
Lin Sun, Jiquan Wang, Yindu Su, Fangsheng Weng, Yuxuan Sun, Zengwei Zheng, and Yuanyi Chen. 2020 · 2020
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Multimodal aspect extraction with region-aware alignment network
Hanqian Wu, Siliang Cheng, Jingjing Wang, Shoushan Li, and Lian Chi. 2020a · 2020
Cited alongside, same era.
Curriculum learning for natural language understanding
Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, and Yongdong Zhang. 2020 · 2020
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Improving multimodal named entity recognition via entity span detection with unified multimodal transformer
Jianfei Yu, Jing Jiang, Li Yang, and Rui Xia. 2020 · 2020
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Multi-modal graph fusion for named entity recognition with targeted visual guidance
Dong Zhang, Suzhong Wei, Shoushan Li, Hanqian Wu, Qiaoming Zhu, and Guodong Zhou. 2021 · 2021
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Object-aware multimodal named entity recognition in social media posts with adversarial learning
Changmeng Zheng, Zhiwei Wu, Tao Wang, Yi Cai, and Qing Li. 2021 · 2021
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Vision-language pre-training for multimodal aspect-based sentiment analysis
Yan Ling, Jianfei Yu, and Rui Xia. 2022 · 2022
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A survey on curriculum learning
Xin Wang, Yudong Chen, and Wenwu Zhu. 2022 · 2022
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Different data, different modalities! reinforced data splitting for effective multimodal information extraction from social media posts
Bo Xu, Shizhou Huang, Ming Du, Hongya Wang, Hui Song, Chaofeng Sha, and Yanghua Xiao. 2022 · 2022
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Face-sensitive image-to-emotional-text cross-modal translation for multimodal aspect-based sentiment analysis
Hao Yang, Yanyan Zhao, and Bing Qin. 2022 · 2022
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Targeted multimodal sentiment classification based on coarse-to-fine grained image-target matching
Jianfei Yu, Jieming Wang, Rui Xia, and Junjie Li. 2022 · 2022
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