arXiv:2309.15670v3 Announce Type: replace
Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...
By Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi, Narayan Ranjan Chakraborty, Vedika Gupta, Mousumi Karmakar
arXiv:2602. 16161v4 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction.
By Rong Fu, Ziming Wang, Shuo Yin, Haiyun Wei, Kun Liu, Xianda Li, Simon Fong
arXiv:2608.29035v1 Announce Type: new
Abstract: Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to pr...
By Thao Le, Michael Thielscher
The YNU-HPCC team participated in Subtask A of SemEval‑2025 Task 11, "Bridging the Gap in Text‑Based Emotion," using a RoBERTa model with a single prediction head to process one emotion at a time. Their system achieved an official ranking score of 0.44 across all languages after translating the dataset into English with Google Translate. Analysis showed that a single head outperformed six simultaneous heads and that training on the uniformly translated English data improved results.
By Hao Yang, Jin Wang, Xuejie Zhang
Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition proposes a hybrid framework that combines a Transformer and a Graph Attention Network to capture both global semantic information and fine-grained relationships between modalities. The model is evaluated on the IEMOCAP and MELD datasets, achieving weighted F1 scores of 72.45% and 77.37%, respectively, and surpasses state‑of‑the‑art methods. These results suggest that integrating multimodal features with balanced global and local context modeling can provide deeper emotional insights for dialogue emotion recognition.
By Jiaqi Qiao, Yifan Lyu, Xiujuan Xu