TASK DEFINITION: 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.
PROBLEM: Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ).  AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.  
 Question: By how much does their method outperform state-of-the-art OOD detection?

SOLUTION: AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average

PROBLEM: Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?

SOLUTION: tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahçe

PROBLEM: Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters. 
 Question: how much was the parameter difference between their model and previous methods?

SOLUTION:
our model requires 100k parameters , while BIBREF8 requires 250k parameters