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]: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.
Question: Is all text in this dataset a question, or are there unrelated sentences in between questions?
[EX A]: the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences

[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 a default train-validation-test split of 70-15-15 for each dataset, and use all 4 datasets (BF, BA, SFU and Sherlock).
Question: Which multiple datasets did they train on during joint training?
[EX A]:
BF, BA, SFU and Sherlock