## 3 Materials and Methods
### Dialogue Act Labeling  
The DAs to label the interview questions were adapted from the Questions DA scheme in [41], with modifications made to the descriptions and guidelines to fit the context of our data, and in general, for the job interview context. These DAs and our modified descriptions of the data are given in Table 1. The interviewees' utterances/responses were classified according to the scheme in Table 2, which is an adaptation of the Answers DA scheme from [41], with the addition of six labels to capture all types of utterances in our dataset: _xx_, _query_, _ft_, _fa_, _fp_, _fe_. The _xx_ label was used to account for any responses that were ambiguous or uninterpretable, and the _query_ label was used to account for interviewee-initiated questions. The last four of the six labels were adapted from [32]. Based on patterns observed in the mock interview's text, a set of in-house guidelines were developed for annotating the questions and the participant utterances according to the DA labeling schemes in Tables 1 and 2.  
Figure 2: The BERT architecture, consisting of stacked encoders; each encoder consists of a self-attention layer followed by a feedforward neural network layer.  
Since the mock interview questions were the same for all the participants, two annotators first labeled the questions according to the scheme in Table 1. Annotators settled any differences in labels via discussions. The DA labels of the questions were then used as a reference to annotate each participant's/interviewee's response to those questions according to the scheme in Table 2. Types of questions often prompt certain responses [63], e.g., a yes-or-no type of question will often prompt a yes or no type of answer, which can make the annotation less ambiguous and can help increase interannotator agreement. The interannotator agreement on the interviewee utterances, after settling differences, was calculated to be 86%, which was the percentage of utterances labeled with same DA by both annotators out of the total number of utterances. The utterances that the two annotators did not agree upon were discarded, and the ones labeled \(xx\) were also discarded, as these were uninterpretable and would be noise in the dataset. Finally, we were left with a dataset  
\begin{table}
\begin{tabular}{c c c} \hline
**Dialogue Act** & **Description** & **Example** \\ \hline _vth_ & Wh-questions, which generally start with “who”, “what”, “where”, “when”, “why”, “how”, etc. & What is your name? \\ _wh-d_ & Wh-declarative questions, when there is more than one statement in a wh-“question. & You said math is your favorite subject. What kind of grades did you get in Math? \\ _bin_ & Binary question, which can be answered with a “yes” or “no”. & Does that sound good? \\ _bin-d_ & Binary-declarative question, which can also be answered with a “yes” or “no”, but has an explanation or a statement before it. & Before getting into some technical questions about the position, tell me, do you have any prior work experience? \\ \multirow{2}{*}{_quo_} & Open question or general questions, not specific to any context, to know the options of the person who is answering. & How do you feel this interview is going? \\ \multirow{2}{*}{_or_} & Choice question, made of two parts connected by conjunction “or”. & Do you have any experience with spreadsheet software such as Microsoft Excel or Google Spreadsheets? \\ \hline \end{tabular}
\end{table}
Table 1: Questions Dialogue Acts.  
Figure 3: Fine-tuning BERT for text classification, as described in [53].  
of 640 annotated interviewee utterances, and the distribution of labels for the 13 classes is shown in Table 3. Note that the dataset of the participants has been deidentified, and contains placeholders '[personName]' for where the participants introduce themselves to the virtual interviewer.