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）因其在现实应用中巨大的潜力，在自然语言处理领域引起了广泛关注。现有ERC方法由于上下文建模不足、对话关系捕获模糊以及说话者建模中的过拟合问题，难以实现对多样化场景的泛化。在本工作中，我们提出了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采用一种混合循环与注意力模块来建模全局情感连续性。接着，我们提出了一种新颖的情感归因编码（EAE），用于对每个话语的内部和跨情感归因进行建模。此外，为了增强模型在说话者建模中的鲁棒性并提升其在不同场景下的表现，我们提出了一种综合损失函数——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者建模的过拟合。我们的模型在三个数据集上取得了最先进的性能，证明了本工作的优越性。同时，我们在三个基准上进行了广泛的对比实验和消融研究，为每个模块的有效性提供了证据。进一步的泛化能力探索实验表明，我们方法中的EAE模块具有即插即用的特性。