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.

[EX Q]:  To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19). 
 Question: What experiments do the authors present to validate their system?
[EX A]:  we measure our system's performance for datasets across various domains evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs

[EX Q]: We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens. 
 Question: What data is used for training CamemBERT?
[EX A]: unshuffled version of the French OSCAR corpus

[EX Q]: The baseline system for the SLC task is a very simple logistic regression classifier with default parameters, where we represent the input instances with a single feature: the length of the sentence.  The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.  
 Question: What was the baseline for this task?
[EX A]:
The baseline system for the SLC task is a very simple logistic regression classifier with default parameters. The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.

