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 compare classification and regression approaches and show that classification produces better results than regression but the quality of the results depends on the approach followed to annotate the data labels. 
 Question: Did classification models perform better than previous regression one?

Output: Yes-no


Input: Consider Input: Also, we compared our models with other existing works on this dataset including OpATT BIBREF6 and Neural Content Planning with conditional copy (NCP+CC) BIBREF4. 
 Question: What is the state-of-the-art model for the task?

Output: Extractive


Input: Consider Input:  In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM.  
 Question: How is the model transferred to other languages?


Output: Abstractive