Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{\rm EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

翻译：对话情感识别（ERC）因其在实际应用中的巨大潜力而受到自然语言处理领域的广泛关注。现有的ERC方法由于对上下文建模不充分、对话关系捕捉模糊以及说话人建模过拟合等问题，难以实现对不同场景的泛化。本文提出一种混合连续归因网络（HCAN），从情感连续性和情感归因的角度解决这些问题。具体而言，HCAN采用混合循环与注意力模块对全局情感连续性进行建模。随后，提出一种新颖的情感归因编码（EAE），对每条话语的内部和跨情感归因进行建模。此外，为增强模型在说话人建模中的鲁棒性并提升其在不同场景下的性能，提出一种综合损失函数——情感认知损失$\mathcal{L}_{\rm EC}$，以缓解情感漂移并克服模型对说话人建模的过拟合。我们的模型在三个数据集上实现了最优性能，证明了工作的优越性。此外，在三个基准数据集上进行了广泛的对比实验和消融研究，以提供证据支持各模块的有效性。进一步泛化能力实验表明，我们的方法中EAE模块具有即插即用的特性。