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.

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.
what is their explanation for the effectiveness of back-translation?

We use the Fisher Spanish speech corpus BIBREF11, which consists of 819 phone calls, with an average duration of 12 minutes, amounting to a total of 160 hours of data.
What language do they look at?

Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database. Goal Generation: a multi-domain goal generator was designed based on the database. The relation across domains is captured in two ways. One is to constrain two targets that locate near each other. The other is to use a taxi or metro to commute between two targets in HAR domains mentioned in the context.  Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals.  Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. 

How was the dataset collected?

