Draft README.md

README.md

@@ -1,3 +1,138 @@
----
-license: cc-by-sa-4.0
----
+---
+language: it
+license: cc-by-sa-4.0
+multilinguality: monolingual
+task_categories:
+- token-classification
+
+tags:
+- Factuality Detection
+- Modality Detection
+---
+# ModaFact - Dataset
+
+## Dataset Description
+
+### Dataset Summary
+
+ModaFact is a textual dataset annotated with Event Factuality and Modality in Italian. ModaFact’s goal is to model in a joint way factuality and modality values of event-denoting expressions in text.
+
+### Textual data source
+
+Original texts (sentences) have been sampled from EventNet-ITA, a dataset for Frame Parsing, which was created using texts from Wikipedia. 
+
+### Annotation
+
+ModaFact has been originally annotated at token level, adopting the IOB2 style.
+Whereas for Modality the schema is unique, for Factuality we provide two representations: a fine-grained representation (FG), which specifies values over three axes (CERTAINTY, POLARITY, TIME), and a coarse-grained representation (CG), which only provides the final factuality value.
+
+
+Example of **coarse-grained representation (CG)**:
+```
+Per	O
+chiarire	B-POSSIBLE-POS-FUTURE-FINAL
+la	O
+questione	O
+la	O
+Santa	O
+Sede	O
+autorizzò	B-CERTAIN-POS-PRESENT/PAST
+il	O
+prelievo	B-UNDERSPECIFIED-POS-FUTURE-CONCESSIVE
+di	O
+campioni	O
+del	O
+legno	O
+che	O
+vennero	O
+datati	B-CERTAIN-POS-PRESENT/PAST
+attraverso	O
+l'	O
+utilizzo	B-CERTAIN-POS-PRESENT/PAST
+del	O
+metodo	O
+del	O
+carbonio-14	O
+.	O
+```
+
+Example of **fine-grained representation (CG)**:
+```
+Per	O
+chiarire	B-NON_FACTUAL-FINAL
+la	O
+questione	O
+la	O
+Santa	O
+Sede	O
+autorizzò	B-FACTUAL
+il	O
+prelievo	B-NON_FACTUAL-CONCESSIVE
+di	O
+campioni	O
+del	O
+legno	O
+che	O
+vennero	O
+datati	B-FACTUAL
+attraverso	O
+l'	O
+utilizzo	B-FACTUAL
+del	O
+metodo	O
+del	O
+carbonio-14	O
+.	O
+```
+
+#### Labelset
+
+Factuality:
+
+- Fine-grained
+  - CERTAINTY: {CERTAIN, PROBABLE, POSSIBLE, UNDERSPECIFIED}
+  - POLARITY: {POSITIVE, NEGATIVE, UNDERSPECIFIED}
+  - TIME: {PRESENT/PAST, FUTURE, UNDERSPECIFIED}
+
+- Coarse-grained
+  - {FACTUAL, NON-FACTUAL, COUNTERFACTUAL, UNDERSPECIFIED}
+
+
+Modality:
+- {WILL, FINAL, CONCESSIVE, POSSIBILITY, CAPABILITY, DUTY, COERCION, EXHORTATIVE, COMMITMENT, DECISION}
+
+
+### Data format
+
+According to the experimental set presented in the paper (see below, Citation Information) we provide different data formats:
+- **token-level BIO sequence labelling**: the dataset is formatted as a two-column `tsv`. The first column contains the token, the second column contains all corresponding labels (factuality and modality), concatenated with `-`. This format makes the dataset ready-to-train with the MaChAmp [seq_bio](https://github.com/machamp-nlp/machamp/blob/master/docs/seq_bio.md) task type.
+- **token-level multi-task sequence labelling**: the dataset is formatted as a three-column `tsv`. The first column contains the token, the second column contains all factuality labels, the third column contains the modality label. This format makes the dataset ready-to-train with the Machamp seq_bio **multitask** setting.
+- **generative and sequence-to-sequence**: the dataset is formatted as a `jsonl` file, containing a list of dictionaries. Each dictionary has an *Input* field (the sentence) and an *Output* field, a string composed by *token=labels* pairs. This format makes the dataset ready-to train with sequence-to-sequence and causal/generative models.
+
+### Data Split
+
+For the sake of reproducibility, we provide, for each configuration, the 5 folds used in the paper.
+The data split follows a 60/20/20 ratio and has been created in a stratified way. This means each train/dev/test set contains (approx) the same relative distribution of classes.
+
+
+
+## Additional Information
+
+
+### Licensing Information
+
+ModaFact is released under the CC-BY-SA-4.0 License. 
+
+### Citation Information
+
+If you use ModaFact, please cite the following paper:
+
+```
+@inproceedings{rovera-et-al-2025-modafact,
+    title = "ModaFact: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection",
+    author = "Rovera, Marco and Cristoforetti, Serena and Tonelli, Sara",
+    booktitle = "COLING2025, tbp",
+    year = "2025",
+	
+}
+```