TASK DEFINITION: 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.
PROBLEM: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .  
 Question: Where are the hotel reviews from?

SOLUTION: TripAdvisor

PROBLEM: We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag). We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN. 
 Question: Do they jointly tackle multiple tagging problems?

SOLUTION: No

PROBLEM: We learn that a number of factors can influence the performance of adversarial attacks, including architecture of the classifier, sentence length and input domain. 
 Question: What other factors affect the performance?

SOLUTION:
architecture of the classifier sentence length  input domain