In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
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Question: Then, we created test sets with varying levels of perturbation operations - $\lbrace 20\%,40\%,60\%\rbrace $. 
 Question: Are recurrent neural networks trained on perturbed data?

Answer: Yes-no


Question: One of the major drawbacks of SCRF models is their high computational cost. In our experiments, the CTC model is around 3-4 times faster than the SRNN model that uses the same RNN encoder. To cut down the computational cost, we investigated if CTC can be used to pretrain the RNN encoder to speed up the training of the joint model. Figure 2 shows the convergence curves of the joint model with and without CTC pretraining, and we see pretraining indeed improves the convergence speed of the joint model. 
 Question: Can SCRF be used to pretrain the model?

Answer: Yes-no


Question: In sub-task C the goal is to classify the target of the offensive language. Only posts labeled as targeted insults (TIN) in sub-task B are considered in this task BIBREF17 . Samples are annotated with one of the following:

Individual (IND): Posts targeting a named or unnamed person that is part of the conversation. In English this could be a post such as @USER Is a FRAUD Female @USER group paid for and organized by @USER. In Danish this could be a post such as USER du er sku da syg i hoved. These examples further demonstrate that this category captures the characteristics of cyberbullying, as it is defined in section "Background" .

Group (GRP): Posts targeting a group of people based on ethnicity, gender or sexual orientation, political affiliation, religious belief, or other characteristics. In English this could be a post such as #Antifa are mentally unstable cowards, pretending to be relevant. In Danish this could be e.g. Åh nej! Svensk lorteret!

Other (OTH): The target of the offensive language does not fit the criteria of either of the previous two categories. BIBREF17 . In English this could be a post such as And these entertainment agencies just gonna have to be an ass about it.. In Danish this could be a post such as Netto er jo et tempel over lort. 
 Question: How many categories of offensive language were there?

Answer:
Abstractive