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: Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12. The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names. 
 Question: Do they report results only on English datasets?
Output: 


Answer: Yes-no