Yumeng Fu, Weitao Huang, Junjie Wu, Hao Teng, Shouduo Shang, Meishan Zhang, Bingquan Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emotion Recognition in Conversation (ERC), the task of identifying the emotion of each utterance in a conversation, is crucial for humanmachine interaction.Existing LLM-based ERC methods focus on standard prompting and slow thinking for emotion analysis.However, they suffer from the lack of human-like emotion reasoning and discrimination between similar emotions, thus limiting accurate emotion predictions.To this end, we present JoPR, jointing perception-curriculum learning and emotional reasoning for conversational emotion recognition.Specifically, we devise a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning.We further design an emotion-specific reward function in a novel reinforcement learning framework, thereby enhancing the discernment between similar emotions.We conduct extensive experiments on three widely used benchmark datasets, and the results demonstrate that our JoPR achieves consistent and significant improvements over baselines.

Yumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu, Bo Zhao, Shaoxuan Ma, Yushun Zhang, Jun Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emotion recognition in conversation (ERC), the task of discerning human emotions for each utterance within a conversation, has garnered significant attention in human-computer interaction systems. Previous ERC studies focus on speaker-specific information that predominantly stems from relationships among utterances, which lacks sufficient information around conversations. Recent research in ERC has sought to exploit pre-trained large language models (LLMs) with speaker modelling to comprehend emotional states. Although these methods have achieved the encouraging results, the extracted speaker-specific information struggles to indicate emotional dynamics. In this paper, motivated by the fact that speaker characteristics play a crucial role and LLMs have rich world knowledge, we present LaERC-S, a novel framework that stimulates LLMs to explore speaker characteristics involving the mental state and behavior of interlocutors, for accurate emotion predictions. To endow LLMs with these knowledge information, we adopt the two-stage learning to make the models reason speaker characteristics and track the emotion of the speaker in complex conversation scenarios. Extensive experiments on three benchmark datasets demonstrate the superiority of LaERC-S, reaching the new state-of-the-art.

Detecting and grounding multi-modal media manipulation (DGM4) aims to identify the authenticity of media content in the form of image-text pairs, and locate forgery contents including text tokens or image regions. Existing methods adopt joint optimization frameworks to train multiple dedicated heads for different sub-tasks. This paradigm is prone to yielding models with insufficient learning regarding manipulation traces. In this paper, we propose a two-stage learning framework, named 2S-DGM4, exploiting correlations between manipulation detection and grounding for debunking manipulation content. It introduces modality-specific manipulation types into the subsequent manipulation grounding, and naturally conducts the mutual agreement among various sub-tasks. Apart from the global context in cross-modal interaction, we develop a fine-grained refinement module to capture subtle manipulation traces. Experiments demonstrate that 2S-DGM4outperforms state-of-the-art methods on the public benchmark dataset DGM4. Further analyses verify the efficacy of 2S-DGM4on manipulation reasoning.

Most defense strategies in complex networks are developed from the defense perspective, overlooking the key attack-defense characteristics in cybersecurity. A defense decision algorithm is ineffective when dealing with dynamic attacking behaviors, and when based on attack-defense analysis, stochastic uniform network models are generally used to model the target network, while most networks are large and complex. Thus, the algorithms and their results do not well suit small-world, scale-free, and high-aggregation networks. In this study, considering the structural characteristics of complex networks and the attack-defense characteristics of cybersecurity, potential differential game theory is integrated with complex networks, and a global optimal defense decision algorithm is proposed according to the overall network defense objective. Based on the evolutionary analysis of network security states, a network attack-defense potential differential game model is constructed. Adversarial analysis is carried out on the overall attack-defense strategy, and a defense decision algorithm is designed based on a saddle point equilibrium strategy. Simulation tests are carried out on small-world and scale-free networks to evaluate the effectiveness of the proposed method by comparing its performance with that of random defense strategies and classic decision algorithms.