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: In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?

Output: The neural projector must be invertible.


Input: Consider Input: Then a comparison to a maximum entropy (MaxEnt) proposed in BIBREF23 type of discriminative model and finally a deep neural network (DNN) architecture proposed in BIBREF24 as reported also in BIBREF22 is presented.

 
 Question: What state-of-the-art models are compared against?


Output: a deep neural network (DNN) architecture proposed in BIBREF24  maximum entropy (MaxEnt) proposed in BIBREF23 type of discriminative model