Update README.md

README.md

@@ -9,6 +9,7 @@ tags:
 - casimedicos
 - explainability
 - medical exams
+- medical question answering
 - multilinguality
 - LLMs
 - LLM
@@ -70,25 +71,27 @@ size_categories:
     <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;">
     <br>
 
-# CasiMedicos-exp: Annotating of Possible Answers Explanations in Resident Medical Exams
+# Antidote CasiMedicos Dataset - Possible Answers Explanations in Resident Medical Exams
 
-We present a new multilingual medical dataset which, in contrast to previous work, includes not only explanatory arguments
+We present a new multilingual parallel medical dataset of commented medical exams which includes not only explanatory arguments
 for the correct answer but also arguments to explain why the remaining possible answers are incorrect.
 
-The data source for our new dataset consists of the Resident Medical Intern exams, or Médico Interno Residente (MIR), originally
+This dataset can be used for various NLP tasks including: **Medical Question Answering**, **Explanatory Argument Extraction** or **Explanation Generation**.
+
+The data source consists of Resident Medical Intern or Médico Interno Residente (MIR) exams, originally
 created by [CasiMedicos](https://www.casimedicos.com), a Spanish community of medical professionals who collaboratively, voluntarily, 
 and free of charge, publishes written explanations about the possible answers included in the MIR exams. The aim is to generate a resource that
 helps future medical doctors to study towards the MIR examinations. The commented MIR exams, including the explanations, are published in the [CasiMedicos 
 Project MIR 2.0 website](https://www.casimedicos.com/mir-2-0/).
 
-We have extracted, clean and annotated the available data so that each document includes the clinical case, the correct answer, the multiple-choice questions and 
-the annotated explanations written by native Spanish medical doctors.
+We have extracted, clean, structure and annotated the available data so that each document in **casimedicos-raw** dataset includes the clinical case, the correct answer, 
+the multiple-choice questions and the annotated explanations written by native Spanish medical doctors.
 
-From the original Spanish version, we have generated **casimedicos-exp**, a multilingual dataset in 4 languages: **English, French, Italian and Spanish**. 
+Furthermore, the original Spanish data has been translated to create a **parallel multilingual dataset** in 4 languages: **English, French, Italian and Spanish**.
 
 <table style="width:33%">
     <tr>
-         <th>casimedicos-exp splits</th>
+         <th>Antidote CasiMedicos splits</th>
      <tr>
          <td>train</td>
          <td>434</td>
@@ -103,7 +106,7 @@ From the original Spanish version, we have generated **casimedicos-exp**, a mult
      </tr>
  </table>
 
-- 📖 Paper: [Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://arxiv.org/abs/2312.00567) In ArXiv 2023.
+- 📖 Paper:[HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine](https://ceur-ws.org/Vol-3516/paper14.pdf)
 - 💻 Github Repo (Data and Code): [https://github.com/ixa-ehu/antidote-casimedicos](https://github.com/ixa-ehu/antidote-casimedicos)
 - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
 - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
@@ -115,12 +118,46 @@ From the original Spanish version, we have generated **casimedicos-exp**, a mult
 <img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;">
 </p>
 
+In this repository you can find the following data:
+
+- **casimedicos-raw**: The textual content including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E) as shown in the example above.
+- **casimedicos-exp**: The manual annotations linking the explanations of the correct and incorrect possible answers.
+
 ## Data Explanation
 
-If you use casimedicos-exp please **cite the following paper**:
+The following attributes composed **casimedicos-raw**:
+
+- **id**: unique doc identifier.
+- **year**: year in which the exam was published by the Spanish Ministry of Health.
+- **question_id_specific**: id given to the original exam published by the Spanish Ministry of Health.
+- **full_question**: Clinical Case (C) and Question (Q) as illustrated in the example document above.
+- **full answer**: Full commented explanation (E) as illustrated in the example document above.
+- **type**: medical speciality.
+- **options**: Possible Answers (P) as illustrated in the example document above.
+- **correct option**: solution to the exam question.
+
+Additionally, the following jsonl attribute was added to create **casimedicos-exp**:
+
+- **explanations**: for each possible answer above, manual annotation states whether (i) the explanation for each possible answer exists in the full comment (E),
+  (ii) if present, then we provide character and token offsets plus the text corresponding to the explanation for each possible answer.
+
 
 ## Citation
 
+If you use the textual content **casimedicos-raw** of the Antidote CasiMedicos dataset then please **cite the following paper**:
+
+```bibtex
+@inproceedings{Agerri2023HiTZAntidoteAE,
+  title={HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine},
+  author={Rodrigo Agerri and I{\~n}igo Alonso and Aitziber Atutxa and Ander Berrondo and Ainara Estarrona and Iker Garc{\'i}a-Ferrero and Iakes Goenaga and Koldo Gojenola and Maite Oronoz and Igor Perez-Tejedor and German Rigau and Anar Yeginbergenova},
+  booktitle={SEPLN 2023: 39th International Conference of the Spanish Society for Natural Language Processing.},
+  year={2023}
+}
+```
+
+Additionally, **cite the previous and the following** paper if you also use **casimedicos-exp**, namely, the manual annotations linking the 
+explanations with the correct and incorrect possible answers ("explanations" attribute in the jsonl data):
+
 ```bibtex
 @misc{goenaga2023explanatory,
       title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams},