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: We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems. 
 Question: What criteria are used to select the 8,640 English sentences?

Output: Sentences involving at least one race- or gender-associated word,  sentence  have to be short and grammatically simple,  sentence have to  include expressions of sentiment and emotion.


Input: Consider Input: Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.  The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. 
 Question: what opportunities are highlighted?


Output: Our annotation scheme introduces opportunities for the educational community to conduct further research  Once automated classifiers are developed, such relations between talk and learning can be examined at scale  automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area