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.

Q: 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.

A: Do they perform a quantitative analysis of their model displaying knowledge distortions?
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Q: One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation.

A: What metrics are considered?
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Q: The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC).

A:
What approaches without reinforcement learning have been tried?
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