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README.md

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+# Diagnostic Interview Corpus
+
+The **Diagnostic interview corpus** is a multilingual dataset of **12,754 French medical diagnostic interview sentences** (questions and instructions) with translations into 12 target languages and a **semantic gloss based on UMLS (Unified Medical Language System)**.
+
+It supports research on:
+
+- Low-resource **multilingual medical machine translation**
+- **Semantic representation** based on UMLS concepts
+- **Generation of pictograph sequences** based on concept sequences for patients with limited health literacy.
+
+To access concept–pictograph pairs, please refer to [this repository](https://doi.org/10.26037/yareta:vo7jydjb25ektpwcvlxwktr72m).
+
+---
+
+## Languages
+
+- **Source:** French
+- **Targets:** The following table lists the target languages together with their codes as used in the datasets. The codes follow **ISO 639-1** when available (e.g., `sq` for Albanian, `es` for Spanish), and **ISO 639-3** or extended conventions when needed (e.g., `prs` for Dari, `ary` for Moroccan Arabic, `en-simple` for Simple English, and `umls` for semantic glosses). 
+
+| Language                  | Code     |
+|---------------------------|----------|
+| Albanian                  | sq       |
+| Modern Standard Arabic    | ar       |
+| Moroccan Arabic           | ary      |
+| Tunisian Arabic           | aeb      |
+| Algerian Arabic (Dziria)  | arq      |
+| Dari                      | prs      |
+| Farsi                     | fa       |
+| Russian                   | ru       |
+| Simple English            | en-simple|
+| Spanish                   | es       |
+| Tigrinya                  | ti       |
+| Ukrainian                 | uk       |
+| UMLS (semantic glosses)   | umls     |
+
+
+- **Semantic gloss:** Representation as a sequence of concepts, using UMLS concepts for medical concepts and custom concepts for functional elements such as agents or modes. Two versions of the gloss are provided: 1) using concept names, 2) using UMLS CUIs (Concept Unique Identifiers) for UMLS concepts and names for custom concepts. Note: as opposed to the CUIs, some UMLS concept names may change over time due to UMLS updates.
+---
+
+## Dataset
+
+The dataset includes two files:
+
+- **`translations.csv`**: French sentences with their human translations into the target languages.
+- **`paraphrases.csv`**: French sentences (same as in translations.csv) with French paraphrases, semantic glosses and diagnostic domain(s).
+
+### translation.csv
+
+#### Data Structure
+
+Each row in the table corresponds to a **target language** (`tgt_lang`) and, where applicable, a **gender variant** (`default` / `female`).
+
+- `sentence_id`: Unique identifier for the source sentence (shared across languages/variants)
+- `src_lang`: Source language code
+- `tgt_lang`: Target language code
+- `gender_variant`: Some target languages in the corpus use **grammatical gender** in ways that affect medical communication. For these cases, the dataset includes **two translation variants**:
+  - `default`: gender-neutral or conventionally masculine (e.g., Albanian _jeni student?_ “Are you a student?”)
+  - `female`: explicitly marked female form (e.g., Albanian _jeni studente?_)
+- `source_text`: Original French sentence
+- `target_text`: Translation into the target language
+- `semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using concept names (UMLS + custom concepts).
+- `CUI_semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using CUIs for UMLS concepts and names for custom concepts (aligned 1:1 with `semantic_gloss`)
+
+#### Distribution
+
+- 12,754 unique French sentences
+- 12 parallel translations
+- Two semantic gloss representations per sentence: one using concept names, and one using CUIs for UMLS concepts
+
+The following table summarises the data by language. Each row corresponds to a **target language** (`tgt_lang`) and, where applicable, a **gender variant** (`default` / `female`).
+
+- `tgt_lang`: Target language code
+- `gender_variant`: `default` or `female`
+- `n_rows`: total rows for this language/variant (from both files)
+- `n_unique_sentences` = unique sentences for this language/variant
+- `total_rows` = overall rows across all gender variants for this language
+- `total_unique_sentences` = overall unique sentences across all gender variants for this language
+
+| tgt_lang  | gender_variant | n_rows | n_unique_sentences | total_rows | total_unique_sentences |
+| --------- | -------------- | ------ | ------------------ | ---------- | ---------------------- |
+| aeb       | default        | 11084  | 11084              | 17072      | 11086                  |
+| aeb       | female         | 5988   | 5988               | 17072      | 11086                  |
+| ar        | default        | 12638  | 12638              | 23105      | 12638                  |
+| ar        | female         | 10467  | 10467              | 23105      | 12638                  |
+| arq       | default        | 11084  | 11084              | 17072      | 11086                  |
+| arq       | female         | 5988   | 5988               | 17072      | 11086                  |
+| ary       | default        | 11084  | 11084              | 17072      | 11086                  |
+| ary       | female         | 5988   | 5988               | 17072      | 11086                  |
+| en-simple | default        | 12602  | 12602              | 12602      | 12602                  |
+| es        | default        | 12662  | 12662              | 12865      | 12662                  |
+| es        | female         | 203    | 203                | 12865      | 12662                  |
+| fa        | default        | 12732  | 12732              | 12732      | 12732                  |
+| prs       | default        | 12711  | 12711              | 12714      | 12711                  |
+| prs       | female         | 3      | 3                  | 12714      | 12711                  |
+| ru        | default        | 11084  | 11084              | 11090      | 11084                  |
+| ru        | female         | 6      | 6                  | 11090      | 11084                  |
+| sq        | default        | 12736  | 12736              | 12752      | 12736                  |
+| sq        | female         | 16     | 16                 | 12752      | 12736                  |
+| ti        | default        | 12711  | 12711              | 24390      | 12711                  |
+| ti        | female         | 11679  | 11679              | 24390      | 12711                  |
+| uk        | default        | 11076  | 11076              | 11080      | 11076                  |
+| uk        | female         | 4      | 4                  | 11080      | 11076                  |
+
+---
+
+### paraphrases.csv
+
+The dataset consists of multiple French paraphrases of the source sentences. French variations created by grammar-based synthetic data generation, which introduces syntactic variation while preserving meaning. Note: these automatically generated sentences are not always fully grammatical.
+
+#### Data Structure
+
+Each row in the dataset represents a **paraphrase** of a French source sentence.
+
+- `sentence_id`: Unique identifier for the source sentence (shared across languages/variants)
+- `src_text`: Original French sentence
+- `paraphrase`: Generated paraphrase of the source. May not always be fully grammatical.
+- `semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using concept names (UMLS + custom concepts).
+- `domains`: Diagnostic domains in which the sentence is used (multiple values separated by `\`).
+- `n_domains`: Number of diagnostic domains associated with the sentence. |
+- `CUI_semantic_gloss`: Semantic representation: pipe-separated sequence of concepts using CUIs for UMLS concepts and names for custom concepts (aligned 1:1 with `semantic_gloss`)
+
+#### Diagnostic domains
+
+- Medical consultations
+- Questions and instructions (e.g., symptom checks, treatment directives)
+- Categories by body region (e.g., head, chest, abdomen)
+
+Each sentence is associated with one or more diagnostic domains, reflecting the **body system** or **clinical situation** covered by the sentence:
+
+- **`checkup`**  
+  General health checkups and preventive care (e.g., routine questions about lifestyle, vaccination, or screening).
+
+- **`chest`**  
+  Respiratory and thoracic conditions (e.g., cough, shortness of breath, chest pain).
+
+- **`covid`**  
+  Sentences specifically related to COVID-19 (e.g., symptoms like fever, loss of smell, quarantine and testing instructions).
+
+- **`dermato`**  
+  Dermatology: skin, hair, and nail conditions (e.g., rashes, infections, wounds, allergies).
+
+- **`drogue`**  
+  Substance use and drug-related issues (e.g., questions about alcohol, tobacco, or illicit drug consumption).
+
+- **`merged_hea_orl_sui`**  
+  Combined domain including the following related domains:
+
+  - **HEA** = Head
+  - **ORL** = Oto-Rhino-Laryngology (ENT: ear, nose, throat)
+  - **SUI** = Suivi (follow-up)
+
+- **`merged_uri_col_abd_anu_sui`**  
+  Combined domain including the following related domains:
+
+  - **URI** = Urinary tract
+  - **COL** = Colon
+  - **ABD** = Abdomen
+  - **ANU** = Anus/rectum
+  - **SUI** = Suivi (follow-up)
+
+- **`suivi`**  
+  Follow-up consultations (e.g., treatment monitoring, recovery questions, long-term care instructions).
+
+- **`traumatologie`**
+  Traumatology: accidents and injuries (e.g., fractures, wounds, burns, emergency trauma-related questions).
+
+---
+
+#### Distribution
+
+The number of rows per diagnostic domain is shown below:
+
+| Domain                     |  Count |
+| -------------------------- | -----: |
+| merged_uri_col_abd_anu_sui | 397225 |
+| merged_hea_orl_sui         | 251558 |
+| chest                      | 190815 |
+| checkup                    | 150490 |
+| dermato                    | 121151 |
+| traumatologie              | 104698 |
+| suivi                      |  45722 |
+| drogue                     |  39561 |
+| covid                      |   6503 |
+
+General statistics of the dataset are given below:
+
+| Metric                           |     Value |
+| -------------------------------- | --------: |
+| Total rows                       | 1,104,502 |
+| Unique paraphrases               | 1,087,150 |
+| Rows with empty `semantic_gloss` |   141,858 |
+
+## Example
+
+```text
+French: Avez-vous des nausées ou des vomissements ?
+English: Do you have nausea or vomiting?
+UMLS gloss: You | Nausea | or – article | Vomiting | Question
+```
+
+---
+
+## Citation
+
+If you use the translations, please cite:
+
+```
+@inproceedings{bouillon-etal-2021-speech,
+    title = "A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility",
+    author = "Bouillon, Pierrette  and
+      Gerlach, Johanna  and
+      Mutal, Jonathan  and
+      Tsourakis, Nikos  and
+      Spechbach, Herv{\'e}",
+    editor = "Field, Anjalie  and
+      Prabhumoye, Shrimai  and
+      Sap, Maarten  and
+      Jin, Zhijing  and
+      Zhao, Jieyu  and
+      Brockett, Chris",
+    booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.nlp4posimpact-1.15/",
+    doi = "10.18653/v1/2021.nlp4posimpact-1.15",
+    pages = "135--142"
+}
+```
+
+```
+@article{info:doi/10.2196/13167,
+	author="Spechbach, Herv{\'e}
+	and Gerlach, Johanna
+	and Mazouri Karker, Sanae
+	and Tsourakis, Nikos
+	and Combescure, Christophe
+	and Bouillon, Pierrette",
+	title="A Speech-Enabled Fixed-Phrase Translator for Emergency Settings: Crossover Study",
+	journal="JMIR Med Inform",
+	year="2019",
+	month="May",
+	day="07",
+	volume="7",
+	number="2",
+	pages="e13167",
+	keywords="anamnesis; emergencies; tools for translation and interpreting; fixed-phrase translator; speech modality",
+	issn="2291-9694",
+	doi="10.2196/13167",
+	url="http://medinform.jmir.org/2019/2/e13167/",
+	url="https://doi.org/10.2196/13167",
+	url="http://www.ncbi.nlm.nih.gov/pubmed/31066702"
+}
+```
+
+If you use the UMLS glosses, please cite:
+
+```
+@inproceedings{gerlach-etal-2024-concept,
+    title = "A Concept Based Approach for Translation of Medical Dialogues into Pictographs",
+    author = "Gerlach, Johanna  and
+      Bouillon, Pierrette  and
+      Mutal, Jonathan  and
+      Spechbach, Herv{\'e}",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.21/",
+    pages = "233--24"
+}
+```
+
+---
+
+## Acknowledgments
+
+This corpus was developed in the context of the BabelDr and PictoDr projects at the University of Geneva, in collaboration with Geneva University Hospitals. This work is part of the PROPICTO project, funded by the Swiss National Science Foundation (N°197864) and the French National Research Agency (ANR-20-CE93-0005). This project also received funding by the ”Fondation Privée des Hôpitaux Universitaires de Genève”.