In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Input: Consider Input: The dataset consists of more than $0.3$ million records and has been made available for future research.
Question: What is the size of the dataset?

Output: $0.3$ million records

Input: Consider Input: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text.
Question: How do they extract topics?

Output: automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15

Input: Consider Input: We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions). It delivers frame-by-frame scores ($\in [0;100]$) for discrete emotional states of joy, anger and fear. We extract the audio signal for the same sequence as described for facial expressions and apply an off-the-shelf tool for emotion recognition. The software delivers single classification scores for a set of 24 discrete emotions for the entire utterance.
Question: What are the emotion detection tools used for audio and face input?
Output: We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions)