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.
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Question: In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier.
Question: What are the components of the multilingual framework?

Answer: text-transformations to the messages vector space model Support Vector Machine

Question: Despite the focus on sharing datasets and source codes on popular software development platforms such as GitHub (github.com) or Zenodo (zenodo.org), it is still a challenge to use data or code from other groups.
Question: Are datasets publicly available?

Answer: Yes

Question: 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.
Question: How big is QA-CTS task dataset?

Answer:
17,833 sentences, 826,987 characters and 2,714 question-answer pairs