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: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.

A: How big is dataset used for fine-tuning BERT?
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Q: Comparing the natural and artificial sources of our parallel data wrt. several linguistic and distributional properties, we observe that (see Fig. FIGREF21 - FIGREF22 ):

artificial sources are on average shorter than natural ones: when using BT, cases where the source is shorter than the target are rarer; cases when they have the same length are more frequent.

automatic word alignments between artificial sources tend to be more monotonic than when using natural sources, as measured by the average Kendall INLINEFORM0 of source-target alignments BIBREF22 : for French-English the respective numbers are 0.048 (natural) and 0.018 (artificial); for German-English 0.068 and 0.053.  The intuition is that properties (i) and (ii) should help translation as compared to natural source, while property (iv) should be detrimental.

A: what is their explanation for the effectiveness of back-translation?
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Q: For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8.

A:
Which languages are used in the paper?
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