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: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision.

Answer: Do they perform a quantitative analysis of their model displaying knowledge distortions?

Question: The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).

Answer: What was the textual source to which OpenIE was applied?

Question: We pair 11'248 standard German written words with their phonetical representations in six different Swiss dialects: Zurich, St. Gallen, Basel, Bern, Visp, and Stans (Figure FIGREF1).

Answer:
How many words are coded in the dictionary?