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.

Input: Consider Input: The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer.  
 Question: Where is a question generation model used?

Output: Extractive


Input: Consider Input: In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2" as the “CORONAVIRUS" type, “bat" and “pangolins" as the “WILDLIFE" type and “Van der Waals forces" as the “PHYSICAL_SCIENCE" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.

In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify “SARS-CoV-2" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as “pylogenetic" as a evolution term and “bat" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as “racism" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation. 
 Question: Did they experiment with the dataset?

Output: Yes-no


Input: Consider Input: We started from an initial corpus that did not have revisions, and kept adding new documents and revising existing documents. The revision operations we used were deletion, addition and replacement of words, sentences, paragraphs, section names and document titles. The addition of words, ..., section names, and new documents were pulled from the Wikipedia abstracts. We generated six data sets using different random seeds, and each data set contained six corpora (Corpus 0 - 5). 
 Question: What are simulated datasets collected?


Output: Abstractive