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: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?

SOLUTION: Abstractive

PROBLEM: We employ precision, recall, F1 and accuracy for evaluation metrics.
Question: what evaluation metrics are reported?

SOLUTION: Extractive

PROBLEM: Secondly, a well-known problem of uneven yet unknown distribution of word senses is alleviated by modifying a naive Bayesian classifier. Thanks to this correction, the classifier is no longer biased towards senses that have more training data. Finally, the aggregated feature values corresponding to target word senses are used to build a naive Bayesian classifier adjusted to a situation of unknown a priori probabilities.
Question: How do they deal with unknown distribution senses?

SOLUTION:
Abstractive