Definition: 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.

Positive Example 1 -
Input: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. 
 Question: How did they obtain the dataset?
Output: Extractive

Positive Example 2 -
Input: We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Output: Yes-no

Negative Example 1 -
Input: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress. 
 Question: What are the corpora used for the task?
Output: Yes-no

Negative Example 2 -
Input: For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them. 
 Question: What details are given about the movie domain dataset?
Output: Extractive

Now complete the following example -
Input: When we annotate dialogues, we should read dialogues from begin to the end. For each utterance, we should find its one parent node at least from all its previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated. We propose three questions for eache dialogue and annotate the span of answers in the input dialogue. As we know, our dataset is the first corpus for multi-party dialogues reading comprehension.

We construct following questions and answers for the dialogue in Example 1:

Q1: When does Bdale leave?

A1: Fri morning

Q2: How to get people love Mark in Mjg59's opinion.

A2: Hire people to work on reverse-engineering closed drivers.

On the other hand, to improve the difficulty of the task, we propose $ \frac{1}{6}$ to $ \frac{1}{3}$ unanswerable questions in our dataset. We annotate unanswerable questions and their plausible answers (PA). Each plausible answer comes from the input dialogue, but is not the answer for the plausible question.

Q1: Whis is the email of daniels?

PA: +61 403 505 896 
 Question: Is annotation done manually?
Output: 


Answer: Yes-no