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Recent advancements in Natural Language Processing (NLP) have highlighted thepotential of sentence embeddings in measuring semantic similarity. Yet, itsapplication in analyzing real-world dyadic interactions and predicting theaffect of conversational participants remains largely uncharted. To bridge thisgap, the present study utilizes verbal conversations within 50 married couplestalking about conflicts and pleasant activities. Transformer-based modelall-MiniLM-L6-v2 was employed to obtain the embeddings of the utterances fromeach speaker. The overall similarity of the conversation was then quantified bythe 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, thisassociation was not observed with husbands' affect regardless of conversationtypes. Two validation checks further provided support for the validity of thesimilarity measure and showed that the observed patterns were not mereartifacts of data. The present study underscores the potency of sentenceembeddings in understanding the association between interpersonal dynamics andindividual affect, paving the way for innovative applications in affective andrelationship sciences.

Decoding Affect in Dyadic Conversations: Leveraging Semantic Similarity through Sentence Embedding