Recent advancements in Natural Language Processing (NLP) have highlighted the potential of sentence embeddings in measuring semantic similarity. Yet, its application in analyzing real-world dyadic interactions and predicting the affect of conversational participants remains largely uncharted. To bridge this gap, the present study utilizes verbal conversations within 50 married couples talking about conflicts and pleasant activities. Transformer-based model all-MiniLM-L6-v2 was employed to obtain the embeddings of the utterances from each speaker. The overall similarity of the conversation was then quantified by the average cosine similarity between the embeddings of adjacent utterances. Results showed that semantic similarity had a positive association with wives' affect during conflict (but not pleasant) conversations. Moreover, this association was not observed with husbands' affect regardless of conversation types. Two validation checks further provided support for the validity of the similarity measure and showed that the observed patterns were not mere artifacts of data. The present study underscores the potency of sentence embeddings in understanding the association between interpersonal dynamics and individual affect, paving the way for innovative applications in affective and relationship sciences.

翻译：自然语言处理（NLP）的最新进展凸显了句子嵌入在测量语义相似性方面的潜力。然而，其在分析现实世界二元互动和预测对话参与者情感方面的应用仍基本未得到探索。为填补这一空白，本研究利用50对已婚夫妇在讨论冲突和愉快活动时的言语对话。采用基于Transformer的模型all-MiniLM-L6-v2获取每位说话者话语的嵌入。随后通过相邻话语嵌入之间的平均余弦相似度量化对话的整体相似性。结果显示，在冲突（而非愉快）对话中，语义相似性与妻子的情感呈正相关。此外，无论对话类型如何，均未观察到这种关联与丈夫的情感相关。两项验证检查进一步支持了相似性度量的有效性，并表明观察到的模式并非数据的人为产物。本研究强调了句子嵌入在理解人际动态与个体情感之间关联方面的潜力，为情感科学和关系科学中的创新应用铺平了道路。