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: Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do.

A: What translationese effects are seen in the analysis?
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Q: Adaptation to food domain captioning Consequently, the food dataset has 3,806 images for training and 1,775 for validation. The non-food dataset has 78,976 images for training and 38,749 for validation.

A: How many examples are there in the target domain?
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Q: . We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score. 

A:
How big are improvements of small-scale unbalanced datasets when sentence representation is enhanced with topic information?
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