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The advent of large language models (LLMs) has gained tremendous attention over the past year.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
A. Zadeh, R. Zellers, E. Pincus, and L.-P. Morency, “Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,” IEEE Intelligent Systems , vol. 31, no. 6, pp. 82–88, Nov. 2016
2016
Earlier work this paper cites.
C.-C. Hsu, S.-Y. Chen, C.-C. Kuo, T.-H. Huang, and L.-W. Ku, “EmotionLines: An emotion corpus of multi-party conversations,” in Proc. the 11th International Conference on Language Resources and Evaluation (LREC) , 2018, pp. 1597–1601
2018
Earlier work this paper cites.
C. Cerisara, S. Jafaritazehjani, A. Oluokun, and H. T. Le, “Multi-task dialog act and sentiment recognition on mastodon,” in Proc. the 27th International Conference on Computational Linguistics (COLING) , 2018, pp. 745–754
2018
Earlier work this paper cites.
H. Pham, T. Manzini, P. P. Liang, and B. Poczós, “Seq2Seq2Sentiment: Multimodal sequence to sequence models for sentiment analysis,” in Proc. Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML) , 2018, pp. 53–63
2018
Earlier work this paper cites.
J. Han, Z. Zhang, and B. W. Schuller, “Adversarial training in affective computing and sentiment analysis: Recent advances and perspectives,” IEEE Computational Intelligence Magazine , vol. 14, no. 2, pp. 68–81, Sep. 2019
2019
Earlier work this paper cites.
D. Ghosal, N. Majumder, S. Poria, N. Chhaya, and A. F. Gelbukh, “DialogueGCN: A Graph Convolutional Neural Network for emotion recognition in conversation,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2019, pp. 154–164
2019
Earlier work this paper cites.
N. Majumder, S. Poria, D. Hazarika, R. Mihalcea, A. F. Gelbukh, and E. Cambria, “DialogueRNN: An attentive RNN for emotion detection in conversations,” in Proc. AAAI Conference on Artificial Intelligence , 2019, pp. 6818–6825
2019
Earlier work this paper cites.
J. Pfeiffer, A. Rücklé, C. Poth, and et al., “Adapterhub: A framework for adapting transformers,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 46–54
2020
Earlier work this paper cites.
W. Yu, H. Xu, F. Meng, Y. Zhu, Y. Ma, J. Wu, J. Zou, and K. Yang, “CH-SIMS: A Chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,” in Proc. the 58th Annual Meeting of the Association for Computational Linguistics (ACL) , 2020, pp. 3718–3727
2020
Cited alongside, same era.
P. Ke, H. Ji, S. Liu, X. Zhu, and M. Huang, “SentiLARE: Sentiment-aware language representation learning with linguistic knowledge,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6975–6988
2020
Cited alongside, same era.
C. T. Heaton and D. M. Schwartz, “Language models as emotional classifiers for textual conversation,” in Proc. the 28th ACM International Conference on Multimedia (MM) , 2020, pp. 2918–2926
2020
Cited alongside, same era.
W. Jiao, M. R. Lyu, and I. King, “Exploiting unsupervised data for emotion recognition in conversations,” in Proc. the 58th Annual Meeting of the Association for Computational Linguistics (ACL) , 2020, pp. 4839–4846
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in Proc. The Tenth International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
J. Zhao, T. Zhang, J. Hu, Y. Liu, Q. Jin, X. Wang, and H. Li, “M3ED: multi-modal multi-scene multi-label emotional dialogue database,” in Proc. the 60th Annual Meeting of the Association for Computational Linguistics (ACL) , 2022, pp. 5699–5710
2022
Later among the works it cites.
J. Guo, J. Tang, W. Dai, Y. Ding, and W. Kong, “Dynamically adjust word representations using unaligned multimodal information,” in Proc. the 30th ACM International Conference on Multimedia (MM) , 2022, pp. 3394–3402
2022
Later among the works it cites.
B. Wang, B. Liang, J. Du, M. Yang, and R. Xu, “SEMGraph: Incorporating sentiment knowledge and eye movement into graph model for sentiment analysis,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2022, pp. 7521–7531
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2020
Cited alongside, same era.
X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang, “P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks,” arXiv preprint arXiv: 2110.07602 , Sep. 2021
2021
Cited alongside, same era.
Z. Yuan, W. Li, H. Xu, and W. Yu, “Transformer-based feature reconstruction network for robust multimodal sentiment analysis,” in Proc. the 29th ACM International Conference on Multimedia (MM) , 2021, pp. 4400–4407
2021
Cited alongside, same era.
J. Tang, K. Li, X. Jin, A. Cichocki, Q. Zhao, and W. Kong, “CTFN: hierarchical learning for multimodal sentiment analysis using coupled-translation fusion network,” in Proc. the 59th Annual Meeting of the Association for Computational Linguistics (ACL) , 2021, pp. 5301–5311
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang, “GLM: general language model pretraining with autoregressive blank infilling,” in Proc. the 60th Annual Meeting of the Association for Computational Linguistics (ACL) , 2022, pp. 320–335
2022
Cited alongside, same era.
2022
Later among the works it cites.
S. Fan, C. Lin, H. Li, Z. Lin, J. Su, H. Zhang, Y. Gong, J. Guo, and N. Duan, “Sentiment-aware word and sentence level pre-training for sentiment analysis,” in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2022, pp. 4984–4994
2022
Later among the works it cites.
B. Xing and I. W. Tsang, “DARER: dual-task temporal relational recurrent reasoning network for joint dialog sentiment classification and act recognition,” in Proc. the 60th Annual Meeting of the Association for Computational Linguistics (ACL) , 2022, pp. 3611–3621
2022
Later among the works it cites.
Z. Zhang, L. Peng, T. Pang, J. Han, H. Zhao, and B. W. Schuller, “Refashioning emotion recognition modelling: The advent of generalised large models,” arXiv preprint arXiv: 2308.11578 , Aug. 2023
2023
Closest in time.
V. Lialin, V. Deshpande, and A. Rumshisky, “Scaling down to scale up: A guide to parameter-efficient fine-tuning,” arXiv preprint arXiv: 2303.15647 , Apr. 2023
2023
Closest in time.
A. Zeng, X. Liu, Z. Du, and etc., “GLM-130B: an open bilingual pre-trained model,” in Proc. International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.