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.
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Question: 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. 

Answer: What datasets are used for experiments?


Question: Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.

Answer: What do you use to calculate word/sub-word embeddings


Question: Results on LangID and NoLangID are compared to the system presented by deri2016grapheme, which is identified in our results as wFST.

Answer:
what was the baseline?