In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[Q]: Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported.
Question: Does pre-training on general text corpus improve performance?
[A]: No

[Q]: To collect article-level labels, we utilized a platform in the company that has been used by the market research team to collect surveys from the subscribers of different news publishers. The survey works as follows: The user is first presented with a set of selected pages (usually 4 pages and around 20 articles) from the print paper the day before. The user can select an article each time that he or she has read, and answer some questions about it. We added 3 questions to the existing survey that asked the level of partisanship, the polarity of partisanship, and which pro- or anti- entities the article presents. We also asked the political standpoint of the user. The complete survey can be found in Appendices.
Question: Did they crowdsource the annotations?
[A]: Yes

[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.
Question: what is their explanation for the effectiveness of back-translation?
[A]:
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