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.

Example Input: Stacked LSTMs Cell-aware Stacked LSTMs
Now we extend the stacked LSTM formulation defined above to address the problem noted in the previous subsection. Sentence Encoders
The sentence encoder network we use in our experiments takes INLINEFORM0 words (assumed to be one-hot vectors) as input. Top-layer Classifiers
For the natural language inference experiments, we use the following heuristic function proposed by BIBREF36 in feature extraction: DISPLAYFORM0

where INLINEFORM0 means vector concatenation, and INLINEFORM1 and INLINEFORM2 are applied element-wise.
Question: Which models did they experiment with?
Example Output: Extractive

Example Input: Comparing these topics with those that appeared on a Catholic women forum, it seems that both ISIS and non-violent groups use topics about motherhood, spousal relationship, and marriage/divorce when they address women. Moreover, we used Depechemood methods to analyze the emotions that these materials are likely to elicit in readers. The result of our emotion analysis suggests that both corpuses used words that aim to inspire readers while avoiding fear. However, the actual words that lead to these effects are very different in the two contexts. Overall, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda that can assist the counterterrorism community.
Question: What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar?
Example Output: Extractive

Example Input: SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21.
Question: What is meant by closed test setting?
Example Output:
Extractive