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: We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.
Question: What baseline is used?

Answer: Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10

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

Question: The SnapCaptions dataset is composed of 10K user-generated image (snap) and textual caption pairs where named entities in captions are manually labeled by expert human annotators (entity types: PER, LOC, ORG, MISC).
Question: Which types of named entities do they recognize?

Answer:
PER, LOC, ORG, MISC