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: We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.

A: What datasets are used for experiments?
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Q: We also perform a fine-grained syntactic analysis that shows how our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them.

A: What conclusions are drawn from the syntactic analysis?
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Q: On inspection, the drop in performance between translated and original Japanese was often a result of translations that were reasonable but not consistent with the labels. For example, when translating the first example in Figure FIGREF2, both machine translations map "UTF8min", which means cold (the illness), into "UTF8min", which means cold (low temperature). Another example is where the Japanese pseudo-tweet "UTF8min" was provided alongside an English pseudo-tweet "Allergy season is so exhausting.". Here, the Japanese word for hay fever "UTF8min" has been manually mapped to the less specific word "allergies" in English; the machine translation maps back to Japanese using the word for "allergies" i.e. "UTF8min" in the katakana alphabet (katakana is used to express words derived from foreign languages), since there is no kanji character for the concept of allergies.

A:
Is there any explanation why some choice of language pair is better than the other?
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