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: Each dataset consists of a collection of records with one QA problem per record. For each record, we include some question text, a context document relevant to the question, a set of candidate solutions, and the correct solution. The context document for each record consists of a list of ranked and scored pseudodocuments relevant to the question. Several baselines rely on the retrieved context to extract the answer to a question. For these, we refer to the fraction of instances for which the correct answer is present in the context as Search Accuracy. Naturally, the search accuracy increases as the context size increases, however at the same time reading performance decreases since the task of extracting the answer becomes harder for longer documents. 
 Question: Which retrieval system was used for baselines?

A: Abstractive
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Q: We test the character and word-level variants by predicting hashtags for a held-out test set of posts. 
 Question: What other tasks do they test their method on?

A: Abstractive
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Q: For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19) 
 Question: How do they identify discussions of LGBTQ people in the New York Times?

A:
Extractive
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