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: To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ).
Question: Do they release MED?

Output: Yes-no

Input: Consider Input: If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model.
Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

Output: Extractive

Input: Consider Input: Methodology ::: Corpus-based Approach
Contextual information is informative in the sense that, in general, similar words tend to appear in the same contexts. In the corpus-based approach, we capture both of these characteristics and generate word embeddings specific to a domain. Methodology ::: Dictionary-based Approach
In Turkish, there do not exist well-established sentiment lexicons as in English. In this approach, we made use of the TDK (Turk Dil Kurumu - "Turkish Language Institution") dictionary to obtain word polarities.
Question: What word-based and dictionary-based feature are used?
Output: Extractive