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.

[Q]: 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?
[A]: 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


[Q]: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously. 
 Question: What type of classifiers are used?
[A]: probabilistic model


[Q]: We collected Japanese-Vietnamese parallel data from TED talks extracted from WIT3's corpus BIBREF15 .  
 Question: did they collect their own data?
[A]:
No