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.

Example Input: Hypothesis 4 (H4) : The proposed template-based synthesis model outperforms a simple word replacement model. Table TABREF13 supports H4 by showing that the proposed synthesis model outperforms the WordNet baseline in training (rows 7, 8 and 19, 20) except Stat2016, and tuning (10, 11 and 22, 23) over all courses. It also shows that while adding synthetic data from the baseline is not always helpful, adding synthetic data from the template model helps to improve both the training and the tuning process. In both CS and ENGR courses, tuning with synthetic data enhances all ROUGE scores compared to tuning with only the original data. (rows 9 and 11). As for Stat2015, R-1 and R-$L$ improved, while R-2 decreased. For Stat2016, R-2 and R-$L$ improved, and R-1 decreased (rows 21 and 23). Training with both student reflection data and synthetic data compared to training with only student reflection data yields similar improvements, supporting H3 (rows 6, 8 and 18, 20). 
 Question: Is the template-based model realistic?  
Example Output: Yes

Example Input: Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats.

Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer);

Untargeted (UNT): Posts containing non-targeted profanity and swearing. Posts with general profanity are not targeted, but they contain non-acceptable language. Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH).

Individual (IND): Posts targeting an individual. It can be a a famous person, a named individual or an unnamed participant in the conversation. Insults and threats targeted at individuals are often defined as cyberbulling.

Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic. Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech.

Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue). 
 Question: What kinds of offensive content are explored?
Example Output: non-targeted profanity and swearing, targeted insults such as cyberbullying, offensive content related to ethnicity, gender or sexual orientation, political affiliation, religious belief, and anything belonging to hate speech

Example Input: To simulate the scenario, we split the Microsoft COCO dataset into food and non-food domain datasets. the food dataset has 3,806 images for training and 1,775 for validation. The non-food dataset has 78,976 images for training and 38,749 for validation. Adaptation to food domain captioning 
 Question: How many examples are there in the source domain?
Example Output:
78,976