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 focus here on the Europarl domain, for which we have ample data in several languages, and use as in-domain training data the Europarl corpus BIBREF5 for two translation directions: English INLINEFORM0 German and English INLINEFORM1 French. When measuring out-of-domain performance, we will use the WMT newstest 2014. or French we use samples from News-Commentary-11 and Wikipedia from WMT 2014 shared translation task, as well as the Multi-UN BIBREF9 and EU-Bookshop BIBREF10 corpora. For German, we use samples from News-Commentary-11, Rapid, Common-Crawl (WMT 2017) and Multi-UN (see table TABREF5 ). 
[A]: what dataset is used?


[Q]: FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet.
[A]: What is the WordNet counterpart for Persian?


[Q]: We show the statistics of the dataset we use in tab:statistics, and the construction procedures will be introduced in this section. In Wikidata BIBREF8 , facts can be described as (Head item/property, Property, Tail item/property).  We first choose the most common 202 relations and 120000 entities from Wikidata as our initial data. ReVerb BIBREF9 is a program that automatically identifies and extracts binary relationships from English sentences. We use the extractions from running ReVerb on Wikipedia. FB15K BIBREF3 is a subset of freebase. TACRED BIBREF10 is a large supervised relation extraction dataset obtained via crowdsourcing. We directly use these two dataset, no extra processing steps were applied.
[A]:
Which knowledge bases do they use?