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.

Ex Input:
We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions. In the first one Wang et al. BIBREF21 downloaded over 5M tweets which included one of 131 emotional hashtags based on Parrott's three-level categorization of emotions in seven categories: joy, sadness, anger, love, fear, thankfulness, surprise.  
 Question: What data is used in experiments?

Ex Output:
Extractive


Ex Input:
Table 2 lists the accuracies of the all methods on two classifier architectures. The results show that, for various datasets on different classifier architectures, our conditional BERT contextual augmentation improves the model performances most. BERT can also augments sentences to some extent, but not as much as conditional BERT does. 
 Question: Do the authors report performance of conditional bert on tasks without data augmentation?

Ex Output:
Yes-no


Ex Input:
In this paper, we propose the first large-scale dataset for QA over social media data.   Table TABREF3 gives an example from our TweetQA dataset. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs 
 Question: What is the size of this dataset?

Ex Output:

Extractive