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:
For comparison, we also report the performance of the error detection system by Rei2016, trained using the same FCE dataset.
Question: What was the baseline used?

Ex Output:
Extractive

Ex Input:
The initial analyses of the embedding matrices for both the UK and France revealed that in general, words were grouped by context or influence on the electricity consumption. For instance, we observed that winter words were together and far away from summer ones.
Question: Is there any example where geometric property is visible for context similarity between words?

Ex Output:
Yes-no

Ex Input:
We use a siamese neural network, shown to perform state-of-the-art few-shot learning BIBREF11, as our baseline model. We modify the original to account for sequential data, with each twin composed of an embedding layer, a Long-Short Term Memory (LSTM) BIBREF12 layer, and a feed-forward layer with Rectified Linear Unit (ReLU) activations.
Question: What were the baselines?

Ex Output:
Abstractive