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: 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?

Output: Extractive

Input: Consider Input: The reports are published by Meteo France and the Met Office, its British counterpart. They are publicly available on the respective websites of the organizations. Both corpora span on the same period as the corresponding time series and given their daily nature, it yields a total of 4,261 and 4,748 documents respectively.
Question: How big is dataset used for training/testing?

Output: Abstractive

Input: Consider Input: These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages. Similar languages are to each other are:
- Nguni languages: zul, xho, nbl, ssw
- Sotho languages: nso, sot, tsn
Question: Which languages are similar to each other?
Output: Extractive