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: Noticeably, despite being both based on and integrated into a BERT$_\text{base}$ model, our architecture even outperforms a standalone BERT$_\text{large}$ model by a large margin. 
 Question: What models other than standalone BERT is new model compared to?

Output: Abstractive


Input: Consider Input: For every input data point and possible target class, LRP delivers one scalar relevance value per input variable, hereby indicating whether the corresponding part of the input is contributing for or against a specific classifier decision, or if this input variable is rather uninvolved and irrelevant to the classification task at all. 
 Question: Does the LRP method work in settings that contextualize the words with respect to one another?

Output: Yes-no


Input: Consider Input: As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?


Output: Abstractive