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: we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics.
Question: Did they only experiment with captioning task?

Output: Yes-no

Input: Consider Input: One of the solutions that has been proposed for mitigating gender bias on the word embedding level is Counterfactual Data Augmentation (CDA) BIBREF25. We apply this method by augmenting our dataset with a copy of every dialogue with gendered words swapped using the gendered word pair list provided by BIBREF21.
Question: How does counterfactual data augmentation aim to tackle bias?

Output: Abstractive

Input: Consider Input: We approached the first and second challenges by using a Bayesian approach to learn which terms were associated with events, regardless of whether they are standard language, acronyms, or even a made-up word, so long as they match the events of interest. The third and fourth challenges are approached by using word-pairs, where we extract all the pairs of co-occurring words within each tweet. To find the words most associated with events, we search for the words that achieve the highest number of spikes matching the days of events.
Question: How are the keywords associated with events such as protests selected?
Output: Abstractive