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:  We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance.   We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required.  
 Question: How does the IPA label data after interacting with users?

Output: It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13  custom actions defined).


Input: Consider Input: Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 
 Question: How are workers trained?

Output: extensive personal feedback


Input: Consider Input: What similarities and/or differences do these topics have with non-violent, non-Islamic religious material addressed specifically to women? As these questions suggest, to understand what, if anything, makes extremist appeals distinctive, we need a point of comparison in terms of the outreach efforts to women from a mainstream, non-violent religious group. For this purpose, we rely on an online Catholic women's forum. Comparison between Catholic material and the content of ISIS' online magazines allows for novel insight into the distinctiveness of extremist rhetoric when targeted towards the female population. To accomplish this task, we employ topic modeling and an unsupervised emotion detection method. 
 Question: How are similarities and differences between the texts from violent and non-violent religious groups analyzed?


Output: By using topic modeling and unsupervised emotion detection on ISIS materials and articles from Catholic women forum