## 3 Materials and Methods
### The Data  
From the distribution in Table 3, we can clearly see that the _sno_ (non-opinionated statements) label comprises almost 60% of the dataset. This distribution is similar to the distribution in the SwDA benchmark dataset, where _sno_ comprises over 50% of the data [40]. The _ft_, _fa_, _fe_, and _fp_ classes have <10 samples each, where _fa_ has only 1 sample. This would have been an issue during the training and testing split, where the _1 fa_ sample could end up in either the training set or the test set, and the other set would not have any samples from the _fa_ class. Instead of random oversampling (creating copies of the minority samples), which is known to lead to overfitting (model trains well on training data, but performs poorly on unseen or test data [64]) [65], we added some new data to the _ft_, _fp_, _fa_, and _fe_ classes. Data for _ft_, _fp_, and _fa_ were gathered from the SwDA dataset, as those were  
\begin{table}
\begin{tabular}{c c c} \hline
**Dialogue Act** & **Description** & **Example** \\ \hline \(y\) & Variations of “yes” answers. & \"yes”, “yeah”, “of course”, “definitely is”, “that’s right”, “1 am sure”, etc. \\ _y-d_ & Yes-answer with an explanation. & Yes. I have experience with Excel. \\ \(n\) & Variations of “no” answers. & \"No, I don’t think so”, “certainly not”, “1 am afraid not”, “not really”, “I don’t have experience...”, “we don’t”, etc. \\ _n-d_ & No-answer with an explanation. & “User didn’t respond”, “I...”. \\ _xx_ & Uninterpretable responses or any responses which look incomplete, such as one word and the user stopped talking. & How do you feel this interview is going? \\ _sno_ & Non-opinionated statements. & I started working on a project the other day. \\ _so_ & Opinionated statements. & Anything which starts with “I think”, “1 believe”, “1 feel”, etc. \\ _ack_ & Acknowledgements. & “okay”, “uh-huh”, “1 see”, etc. \\ _dno_ & It is a response given when the person is unsure, doesn’t know, or doesn’t recall. & “I don’t know”, “maybe”, “1 guess”, “I suppose”, etc. \\ _query_ & Interviewee-initiated question. & What is the work environment like? \\ _ft_ & Thank yous & “thanks”, “thank you” \\ _fa_ & Apologies. & “I’m sorry” \\ _fe_ & Exclamations. & “shoot”, “oh goodness”, “jeez”, etc. \\ _fp_ & Greetings or conventional openings. & “hello”, “nice to meet you” \\ \hline \end{tabular}
\end{table}
Table 2: Answers Dialogue Acts.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dialogue Act** & **No. of Samples** & **Percentage Distribution (\%)** \\ \hline \(y\) & 50 & 7.81 \\ _y-d_ & 17 & 2.66 \\ \(n\) & 49 & 7.66 \\ _n-d_ & 10 & 1.56 \\ _sno_ & 381 & 59.53 \\ _so_ & 30 & 4.69 \\ _ack_ & 13 & 2.03 \\ _dno_ & 44 & 6.88 \\ _query_ & 34 & 5.31 \\ _ft_ & 3 & 0.47 \\ _fa_ & 1 & 0.16 general cases of sorry, greetings, and thank you. After isolating the utterances in those classes from the SwDA corpus and removing duplicates, we ended up with 38 samples of _ft_, 57 samples of _fp_, and 41 samples of _fa_. For the _fe_ class, we gathered samples from a corpus developed in our previous work, which included exclamation-type words spelled specifically as captured by the Microsoft Azure Speech-to-Text service. For example, curse words are censored by Azure Speech, with the asterisk \"*\" character replacing the characters (e.g., ****), and other words are spelled differently, such as \"jeez\", which is spelled as \"geez\" when transcribed by Azure. After removing duplicates, we ended up with 51 samples of _fe_. We added these to the original 640 sample dataset, which led to an 827-sample dataset. To prepare the dataset for model training, we removed most punctuation with the exception of the following: '?', which was found in interviewee-initiated queries; '('and ')', which were used for the placeholder '[personName]'; '*', which was left for the curse words that were censored by Azure's Speech-to-Text transcriber; and \"'\", apostrophes used to preserve \"'s \" or \"I'm\" in utterances. The utterances were then converted to lowercase, which made our final dataset.