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: We have used a dataset of 14 TDs to conduct our experiments.
Question: Which dataset(s) do they use?

SOLUTION: 14 TDs BIBREF15

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: In contrast to existing bottlenecks, this work targets three different types of social networks (Formspring: a Q&A forum, Twitter: microblogging, and Wikipedia: collaborative knowledge repository) for three topics of cyberbullying (personal attack, racism, and sexism) without doing any explicit feature engineering by developing deep learning based models along with transfer learning.
Question: What cyberbulling topics did they address?

SOLUTION:
personal attack, racism, and sexism