The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network,~namely DAG-ERC, to implement this idea.~In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models,~DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context.~Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison.~The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.

翻译：建立对话环境的模型,在对话中的情感识别(ERC)中发挥着至关重要的作用。 在本文中,我们提出了一个新颖的想法,用定向单程图(DAG)将言论编码,以更好地模拟对话中的内在结构,并设计定向单程神经网络,即DAG-ERC,以落实这一想法。 ~ 为了将传统的基于图形的神经模型和反复出现的神经模型的优势结合起来,~DAG-ERC为模拟长程对话背景和附近背景之间的信息流动提供了更直观的方法。~ 对四个ECRC基准进行了广泛的实验,使用最先进的模型作为比较基线。~ 实验结果显示了这一新模型的优越性,并证实了为ERC设计的定向单程图表结构的动力。