metadata

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, consisting of annotated sentences from Wikipedia.

Statistics

Feature	#
Sentences	3,039
Words	73,784
Annotations	10,445
Unique label assignments	33,029
Words per sentence (avg.)	24.28
Annotations per sentence (avg.)	3.44
Unique label assignments per sentence	10.87

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 fine-grained representation (FG):

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 coarse-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 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, separated by |. 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

An instance of the mT5 model, fine-tuned on ModaFact, is available at this repo.

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-etal-2025-modafact,
    title = "{M}oda{F}act: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection",
    author = "Rovera, Marco  and
      Cristoforetti, Serena  and
      Tonelli, Sara",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.425/",
    pages = "6378--6396",
    }