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.

Q: Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.

A: Does the use of out-of-domain data improve the performance of the method?
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Q: Task: given a caption and a paired image (if used), the goal is to label every token in a caption in BIO scheme (B: beginning, I: inside, O: outside) BIBREF27 .

A: Can named entities in SnapCaptions be discontigious?
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Q: The second series of tests consisted of using all the documents of the subcorpus "specialist" $E$, because the documents of the subcorpus of Annodis are not identical. As far as system evaluations are concerned, we use the 78 $E$ documents as reference. We calculate the precision $P$, the recall $R$ and the $F$-score on the text corpus used in our tests, as follow:

A:
How is segmentation quality evaluated?
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