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.

Q: We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1. 
 Question: Do they specify the model they use for Gunrock?

A: Yes-no
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Q: 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?

A: Extractive
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Q: Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present.  
 Question: How do they collect the control corpus?

A:
Abstractive
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