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:
Following the formula, we can calculate the contribution of every input word makes to every output word, forming a contribution matrix of size $M \times N$, where $N$ is the output sentence length. Given the contribution matrix, we can obtain the word importance of each input word to the entire output sentence. To this end, for each input word, we first aggregate its contribution values to all output words by the sum operation, and then normalize all sums through the Softmax function. 
 Question: How do their models decide how much improtance to give to the output words?

Ex Output:
Extractive


Ex Input:
In recent years, there has been a rapid growth in the usage of social media. People post their day-to-day happenings on regular basis. BIBREF0 propose four tasks for detecting drug names, classifying medication intake, classifying adverse drug reaction and detecting vaccination behavior from tweets. We participated in the Task2 and Task4. 
 Question: Was the system only evaluated over the second shared task?

Ex Output:
Yes-no


Ex Input:
We describe our rules for WikiSQL here. We first detect WHERE values, which exactly match to table cells. After that, if a cell appears at more than one column, we choose the column name with more overlapped words with the question, with a constraint that the number of co-occurred words is larger than 1. By default, a WHERE operator is INLINEFORM0 , except for the case that surrounding words of a value contain keywords for INLINEFORM1 and INLINEFORM2 . Then, we deal with the SELECT column, which has the largest number of co-occurred words and cannot be same with any WHERE column. By default, the SELECT AGG is NONE, except for matching to any keywords in Table TABREF8 . The coverage of our rule on training set is 78.4%, with execution accuracy of 77.9%. Our rule for KBQA is simple without using a curated mapping dictionary. The pipeline of rules in SequentialQA is similar to that of WikiSQL. Compared to the grammar of WikiSQL, the grammar of SequentialQA has additional actions including copying the previous-turn logical form, no greater than, no more than, and negation. 
 Question: How many rules had to be defined?

Ex Output:

Abstractive