Summary of Casimedicos-arg: a Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures, by Ekaterina Sviridova et al.
CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures
by Ekaterina Sviridova, Anar Yeginbergen, Ainara Estarrona, Elena Cabrio, Serena Villata, Rodrigo Agerri
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing need to explain Artificial Intelligence (AI) decisions, particularly in sensitive domains like medicine and law. The authors recognize that human-based deliberation also requires justifying decision-making processes. To aid medical residents train their explanation skills, this research presents the first multilingual dataset for Medical Question Answering. This dataset features 558 clinical cases in four languages (English, Spanish, French, Italian) with correct and incorrect diagnoses enriched with natural language explanations written by doctors. The explanations are annotated with argument components (premise, claim) and relations (attack, support). The Multilingual CasiMedicos-Arg dataset comprises 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations. Competitive baselines are evaluated on this challenging dataset for the argument mining task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to explain why a doctor made a certain diagnosis. It’s not just about getting it right, but also about showing how you got there. This is a big problem in medicine and other fields where decisions need to be justified. To help solve this issue, researchers created a special dataset that includes medical cases with correct and incorrect diagnoses, along with explanations written by doctors. The dataset has 558 cases in four languages, with words that make sense put together in the right way. This can help machines learn how to explain their decisions better. |
Keywords
» Artificial intelligence » Question answering
