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: While the mixing strategy compensates for most of the gap between the Fr-De* and the Fr*-De (3.01 $\rightarrow $ 0.17) in the De $\rightarrow $ Fr case, the resulting PSEUDOmix still shows lower BLEU than the target-originated Fr-De* corpus. We thus enhance the quality of the synthetic examples of the source-originated Fr*-De data by further training its mother translation model (En $\rightarrow $ Fr). As illustrated in Figure 2 , with the target-originated Fr-De* corpus being fixed, the quality of the models trained with the source-originated Fr*-De data and PSEUDOmix increases in proportion to the quality of the mother model for the Fr*-De corpus. Eventually, PSEUDOmix shows the highest BLEU, outperforming both Fr*-De and Fr-De* data.  As presented in Table 6 , we observe that fine-tuning using ground truth parallel data brings substantial improvements in the translation qualities of all NMT models. Among all fine-tuned models, PSEUDOmix shows the best performance in all experiments. This is particularly encouraging for the case of De $\rightarrow $ Fr, where PSEUDOmix reported lower BLEU than the Fr-De* data before it was fine-tuned. Even in the case where PSEUDOmix shows comparable results with other synthetic corpora in the Pseudo Only scenario, it shows higher improvements in the translation quality when fine-tuned with the real parallel data.  
 Question: How many improvements on the French-German translation benchmark?
Output: 


Answer: Abstractive