Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language models (LLMs), excel at capturing semantic correlations within utterance embeddings but fall short in determining the specific causal relationships. To overcome this limitation, we propose the incorporation of \textit{i.i.d.} noise terms into the conversation process, thereby constructing a structural causal model (SCM). It explores how distinct causal relationships of fitted embeddings can be discerned through independent conditions. To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable "implicit causes." Moreover, we curate a synthetic dataset that includes i.i.d. noise. Through comprehensive experiments, we validate the effectiveness and interpretability of our approach. Our code is available in https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper.

翻译：我们对对话情感推理任务的研究揭示了因果区分这一挑战。现有模型(包括大型语言模型)虽擅长捕捉话语嵌入中的语义相关性,但在确定具体因果关系方面表现不足。为克服这一局限,我们提出在对话过程中引入独立同分布噪声项,从而构建结构因果模型。该模型通过独立条件探索如何区分拟合嵌入的不同因果关系。为便于深度学习实现,我们引入cogn框架处理非结构化对话数据,并采用自编码器架构将不可观测噪声视为可学习的"隐式原因"。此外,我们构建了包含独立同分布噪声的合成数据集。通过全面实验,我们验证了该方法的效果与可解释性。代码已开源在https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper。