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.

Input: Consider Input: To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.
Question: How do they confirm their model working well on out-of-vocabulary problems?

Output: conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set

Input: Consider Input: For de-identification tasks, the three metrics we will use to evaluate the performance of our architecture are Precision, Recall and INLINEFORM0 score as defined below.
Question: What evaluation metrics do they use?

Output: Precision, Recall and INLINEFORM0 score

Input: Consider Input: SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21.
Question: What is meant by closed test setting?
Output: closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation