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.
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Question: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier.

Answer: what types of features were used?


Question: 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.

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


Question: However, adversarial misspellings constitute a longstanding real-world problem. Spammers continually bombard email servers, subtly misspelling words in efforts to evade spam detection while preserving the emails' intended meaning BIBREF1 , BIBREF2 .

Answer:
Why is the adversarial setting appropriate for misspelling recognition?