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 Q]: We collected Japanese-Vietnamese parallel data from TED talks extracted from WIT3's corpus BIBREF15 .  
 Question: did they collect their own data?
[EX A]: Yes-no

[EX Q]: Irony Classifier: We implement a CNN classifier trained with our irony dataset. Sentiment Classifier for Irony: We first implement a one-layer LSTM network to classify ironic sentences in our dataset into positive and negative ironies. Sentiment Classifier for Non-irony: Similar to the training process of the sentiment classifier for irony, we first implement a one-layer LSTM network trained with the dataset for the sentiment analysis of common twitters to classify the non-ironies into positive and negative non-ironies. In this section, we describe some additional experiments on the transformation from ironic sentences to non-ironic sentences. 
 Question: What experiments are conducted?
[EX A]: Extractive

[EX Q]: We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). 
 Question: What baselines did they consider?
[EX A]:
Extractive

