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.

Example Input: The resulting set of 46 documents makes up our base corpus. Note that these documents vary greatly in size, which means the resulting corpus is varied, but not balanced in terms of size (Table TABREF43).
Example Output: How big PIE datasets are obtained from dictionaries?

Example Input: To test if the learned representations can separate phonetic categories, we use a minimal pair ABX discrimination task BIBREF19 , BIBREF20 . For the within-speaker task, all the phones triplets belong to the same speaker (e.g. $x$3 ) Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, $x$4 and $x$5 belong to the same speaker, and $x$6 to a different one (e.g. $x$7 ). The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.
Example Output: What is the metric that is measures in this paper?

Example Input: While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time
Example Output:
How is an incoming claim used to retrieve similar factchecked claims?