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]: Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9. 
 Question: What was the baseline?
[EX A]: Extractive

[EX Q]: Our system, including software and corpus, is available as an open source project for free research purpose and we believe that it is a good baseline for the development and comparison of future Vietnamese SRL systems.  
 Question: Are their corpus and software public?
[EX A]: Yes-no

[EX Q]: The improved performance of our attention models that actively select their optimal context, over a model with the complete thread as context, hLSTM, shows that the context inference improves intervention prediction over using the default full context. 
 Question: What aspects of discussion are relevant to instructor intervention, according to the attention mechanism?
[EX A]:
Extractive