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.

Example Input: 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.
Example Output: what is their explanation for the effectiveness of back-translation?

Example Input: We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
Example Output: How was performance of previously proposed rules at very large scale?

Example Input: In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive).
Example Output:
How does their model learn using mostly raw data?