###### Abstract
Hence, automatic labeling to recognize the meaning of utterances has the potential to enhance current simulators for training autistic individuals. We address this via automated dialogue act classification. Dialogue act classification or recognition is a method where utterances of a conversation are classified as one of several dialogue acts or speech acts [26]. A dialogue act (DA) or speech act represents the meaning or intent of an utterance, e.g., whether the utterance is a question, a yes or no response, a \"thank you\" or a \"good-bye\", or a statement of opinion or fact [27]. Hence, DAs can capture the semantics of utterances in a domain by mapping utterances to DA. DA classification has several applications, such as intent mining and identification [28], classifying issues from comments on online collaborative platforms such as GitHub [29] in conversation agents and dialogue systems for an agent/system to understand the user and execute tasks that drive the flow of dialogue [30, 31, 32, 33]. When combined with conversation context, DA classification has shown to improve contextual topic modeling (extraction of themes or topics in text [34]) [35]. When automated, DA classification can be used to automatically label utterances, whether from text or other modes of data, such as text combined with facial expressions [36, 37] or speech signals [32, 38]. During the development of our previous work [39], we encountered a lack of publicly available data to train a text classifier that classifies utterances into domain-independent DA that are relevant in a job interview context. Hence, in response to the aforementioned gap, we collected utterances of participants responding to questions from an interview script while interacting with our virtual job interview platform described in [39]. This data collection study resulted in a small dataset of 640 utterances. Observing patterns in the responses to interview questions, we developed a DA labeling scheme based on previous works [32, 40, 41] that captures the semantics of utterances in the context of a job interview regardless of the domain of the interview. The anonymized data were then labeled by two annotators according to the developed DA scheme. These annotated data were then used to train the classifier. First, a baseline support vector machine (SVM) classifier and a random forest (RF) classifier were trained on the annotated data, which helped understand our data and highlighted where traditional machine learning failed to classify some labels. Second, a pretrained bidirectional encoder representations from transformers (BERT) model was fine-tuned using transfer learning on our annotated data (building on the work of [41]). The final BERT classifier performed with a 0.92 accuracy and an f1-score of 0.87. The scope of this article is twofold: (1) to introduce a DA scheme to capture semantics of utterances in a job interview context through data collected in an experimental study with autistic individuals using our virtual reality job interview training system [39]; and (2) to present the best-performing classifier on the data. The three main contributions of this paper are (1) an original dataset comprising annotated responses to job interview questions by autistic individuals of working age, where responses are labeled using an in-house dialogue act scheme for job interviews; (2) a predictive model that takes an interview response as input and outputs the dialogue act to understand the general intent of the response; and (3) the interview script and annotation guidelines to help future developers replicate this work and collect more data. In this work, we aim to introduce a new autistic dataset with real speech data collected from a simulated job interview that can be used for automated DA classification in VR simulations to aid autistic individuals. It is to be noted that we have adapted a standard BERT model for DA classification of utterances to demonstrate feasibility as a first step towards this aim.