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.

Example Input: As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (42–49 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape. 
 Question: What is proof that proposed functional form approximates well generalization error in practice?
Example Output: estimated test accuracy is highly correlated with actual test accuracy for various datasets appropriateness of the proposed function for modeling the complex error landscape

Example Input: We apply attention probes to the task of identifying the existence and type of dependency relation between two words. Our first experiment is an exploratory visualization of how word sense affects context embeddings.  
 Question: What linguistic features were probed for?
Example Output: dependency relation between two words word sense

Example Input: In addition to the traditional VAE structure, we introduces an extra context-aware latent variable in CWVAE to learn the event background knowledge. In the pretrain stage, CWVAE is trained on an auxiliary dataset (consists of three narrative story corpora and contains rich event background knowledge), to learn the event background information by using the context-aware latent variable. Subsequently, in the finetune stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target (e.g., intents, reactions, etc.). 
 Question: How does the context-aware variational autoencoder learn event background information?
Example Output:
 CWVAE is trained on an auxiliary dataset to learn the event background information by using the context-aware latent variable.  Then, in finetute stage, CWVAE is trained on the task-specific dataset to adapt the event background information to each specific aspect of If-Then inferential target.