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.

“推特机器人检测任务的目标是判断一个推特账号是真人账号还是自动化机器人账号。随着自动化账号拟人算法的快速迭代,检测最新类别的自动化账号变得越来越困难。最近,预训练语言模型在自然语言生成任务和其他任务上表现出了出色的水平,当这些预训练语言模型被用于推特文本自动生成时,会为推特机器人检测任务带来很大挑战。本文研究发现,困惑度偏低和相似度偏高的现象始终出现在不同时代自动化账号的历史推文中,且此现象不受预训练语言模型的影响。针对这些发现,本文提出了一种抽取历史推文困惑度特征和相似度特征的方法,并设计了一种特征融合策略,以更好地将这些新特征应用于已有的算法模型。本文方法在选定数据集上的性能超越了已有的基准方法,并在人民网主办、传播内容认知全国重点实验室承办的社交机器人识别大赛上取得了冠军。”