In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.

Ex Output:
How big is new question answering dataset?


Ex Input:
The release of the FEVER fact extraction and verification dataset BIBREF0 provides a large-scale challenge that tests a combination of retrieval and textual entailment capabilities. To verify a claim in the dataset as supported, refuted, or undecided, a system must retrieve relevant articles and sentences from Wikipedia. Then it must decide whether each of those sentences, or some combination of them, entails or refutes the claim, which is an entailment problem. Systems are evaluated on the accuracy of the claim predictions, with credit only given when correct evidence is submitted. As entailment data, premises in FEVER data differ substantially from those in the image caption data used as the basis for the Stanford Natural Language Inference (SNLI) BIBREF1 dataset. Sentences are longer (31 compared to 14 words on average), vocabulary is more abstract, and the prevalence of named entities and out-of-vocabulary terms is higher. The retrieval aspect of FEVER is not straightforward either. A claim may have small word overlap with the relevant evidence, especially if the claim is refuted by the evidence.

Ex Output:
What is the FEVER task?


Ex Input:
 the TransE model Embedding models The Unstructured model BIBREF22 The TransH model BIBREF26 DISTMULT BIBREF45 ConvE BIBREF51 and ConvKB BIBREF52 The Path Ranking Algorithm (PRA) BIBREF21

Ex Output:

What models does this overview cover?