instruction:
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.
question:
the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 ). In summary, our full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting implicit discourse relations, which outperforms previous neural tensor network models (e.g., BIBREF18 ) by more than 2 percents and outperforms the best previous system BIBREF19 by 1 percent. Then we also created ensemble models by applying majority voting to combine results of ten runs. From table 5 , each ensemble model obtains performance improvements compared with single model. The full model achieves performance boosting of (51.84 - 48.82 = 3.02) and (94.17 - 93.21 = 0.96) in macro F1-scores for predicting implicit and explicit discourse relations respectively.  
 Question: How much does this model improve state-of-the-art?
answer:
Extractive


question:
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: What are the selection criteria for "causal statements"?
answer:
Abstractive


question:
For every gendered character in the dataset, we ask annotators to create a new character with a persona of the opposite gender that is otherwise identical except for referring nouns or pronouns. 
 Question: In the targeted data collection approach, what type of data is targetted?
answer:

Abstractive