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.

[Q]: Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API.
Question: Do they evaluate only on English datasets?
[A]: Yes

[Q]: We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset.
Question: What dataset is used?
[A]: Twitter dataset obtained from the authors of BIBREF12

[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?
[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