Identifying Small Talk in Natural Conversations (2025)

Abstract: Small talk is part and parcel of human interaction and is rather employed to communicate values and opinions than pure information. Despite small talk being an omnipresent phenomenon in spoken language, it is difficult to identify: Small talk is situated, i.e., for interpreting a string of words or discourse units, outside references such as the context of the interlocutors and their previous experiences have to be interpreted.In this paper, we present a dataset of natural conversation annotated with a theoretically well-motivated distillation of what constitutes small talk. This dataset comprises of verbatim transcribed public service encounters in German authorities and are the basis for empirical work in administrative policy on how the satisfaction of the citizen manifests itself in the communication with the authorities. We show that statistical models achieve comparable results to those of state-of-the-art LLMs.

## introduction small talk is an omnipresent phenomenon when people interact with each other. there is a variety of reasons why people engage in small talk, for instance to exhibit politeness, to build a connection with strangers or to start a conversation. from a linguistic point of view, small talk is a highly interesting type of conversation, for it is not primarily focused on the exchange of informationone could even argue that the topic of the conversation does not really matter -but rather about the exchange of values and opinions. from a computational point of view, small talk is a challenging phenomenon because it is highly context dependent, i.e., the individual background of the interlocutors together with the situational context determines the scope and content of the small talk. the genre they mostly appear in, namely conversations, is underrepresented in terms of resources overall, but in terms of small talk in particular. but small talk is crucial for socio-linguistic analyses of conversations. the source of the data in this paper are public service encounters in germany @xcite , i.e., direct conversations between citizens and representations of the state where citizens ask for support or benefits from the representatives. previous work in administrative policy shows that even if the decision of the state is not in favor of the citizen, emphatic communication yields satisfaction scores that parallel those of favorable decisions @xcite . therefore, being able to measure and identify relationbuilding blocks of conversation paves the way for meaningful sociolinguistic analyses of conversations at scale. the challenges are two-fold: from a theoretical point of view, concrete definitions for the concept of small talk are lacking, making the process of generating annotation guidelines tricky. moreover, small talk is mostly performed in conversations -those are time-consuming to record and to transcribe, making sufficient training data expensive. the contributions of this paper are three-fold: first, we put forward theoretically-motivated annotation guidelines that can be used to annotate small talk in transcribed conversations. we also present a new, human-annotated small talk dataset containing more than 2,600 utterances from german public service encounters. lastly, we show that statistical models such as logistic regression or support vector machines achieve results comparable to state-of-the-art llms after thorough training. our error analysis demonstrates the difficulties of classifying small talk automatically. ## predicting small talk 4.1 training we use four different models to identify small talk, two statistical models (logistic regression and svm) and two language models (gbert, gpt-4) to see how more expensive models fare in comparison with smaller models. the baseline is logistic regression, with tf-idf vectorization for training and test set (german stopwords are removed by the vectorizer) and with sentence embeddings from paraphrase-multilingual-minilm-l12-v2 @xcite . for svm, we again use tf-idf versus sentence embeddings and conduct 5-fold cross-validation with standardscaler from scikit-learn. finetuning is performed using gridsearchcv. again, both tf-idf vectors and sentence embeddings are used for vectorization. the first language model is gbert, a bert model specifically trained on german data @xcite , with the optimal settings determined by gridsearchcv (see ## conclusion the error analysis has shown which problems remain in the classification of small talk. complex classification models such as neural networks and transformer-based models are less suitable for this task until more training data is available. llms achieve in general good results in classifying the data, but prompting is the only way to control the classification. simple classification models are labor-intensive as they have to be precisely finetuned. ultimately, however, they provide the most transparent classifications and -at least in our study -achieved results comparable to those of llms.