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:
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers.
Question: what ml based approaches were compared?

Ex Output:
Extractive

Ex Input:
Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers.
Question: How big is dataset of car-speak language?

Ex Output:
Extractive

Ex Input:
Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n
brace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr
brace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes string-matching (str), precise-construct (prec), and attribute-matching (attr) and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .

$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions.
Question: What are resolution model variables?

Ex Output:
Abstractive