TASK 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.
PROBLEM: For comparison, we also report the performance of the error detection system by Rei2016, trained using the same FCE dataset. 
 Question: What was the baseline used?

SOLUTION: Extractive

PROBLEM: In this section, we present the details of the analysis performed on the data obtained pertaining to Twitter messages from January 2020 upto now, that is the time since the news of the Coronavirus outbreak in China was spread across nations. Also, the data prominently captures the tweets in English, Spanish, and French languages. 
 Question: Do they collect only English data?

SOLUTION: Yes-no

PROBLEM: The GENIA Corpus BIBREF3 contains biomedical abstracts from the PubMed database. We use GENIA technical term annotations 3.02, which cover linguistic expressions to entities of interest in molecular biology, e.g. proteins, genes and cells. CoNLL2003 BIBREF14 is a standard NER dataset based on the Reuters RCV-1 news corpus. It covers named entities of type person, location, organization and misc.

For testing the overall annotation performance, we utilize CoNLL2003-testA and a 50 document split from GENIA.  
 Question: what standard dataset were used?

SOLUTION:
Extractive