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.

Input: Consider Input: Davidson et al. BIBREF2 showed that there were systematic and substantial racial biases in five benchmark Twitter datasets annotated for offensive language detection. By examining more samples and with respect to recently studies BIBREF2, BIBREF24, BIBREF19, it is clear that many errors are due to biases from data collection BIBREF19 and rules of annotation BIBREF24 and not the classifier itself.  
 Question: What are the existing biases?

Output: Extractive


Input: Consider Input: Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting.  Note that “big” datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15  have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small. 
 Question: Why is big data not appropriate for this task?

Output: Abstractive


Input: Consider Input: To help the human representative quickly determine the cause of the escalation, we generate a visualization of the user's turns using the attention weights to highlight the turns influential in the escalation decision. 
 Question: Do they explain model predictions solely on attention weights?


Output: Yes-no