GiliGold/knesset-dicta-checkworthiness

This model is based on Knesset-dictaBERT and was trained to classify a Hebrew sentence for checkworthiness.

The possible values are: worth checking, not worth checking , or not a factual proposition

It was trained on a train-set of ~5000 manually annotated sentences from the Knesset Corpus.

The train set is available here.

The Knesset Corpus automatically annotated for checkworthiness by knesset-dicta-checkworthiness is available here

Paper: ArXiv paper

• Citation:

@inproceedings{goldin-etal-2025-annotation,
    title = "An Annotation Scheme for Factuality and Its Application to Parliamentary Proceedings",
    author = "Goldin, Gili  and
      Wigderson, Shira  and
      Rabinovich, Ella  and
      Wintner, Shuly",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2025.ranlp-1.49/",
    pages = "403--412",
    abstract = "Factuality assesses the extent to which a language utterance relates to real-world information; it determines whether utterances correspond to facts, possibilities, or imaginary situations, and as such, it is instrumental for fact checking. Factuality is a complex notion that relies on multiple linguistic signals, and has been studied in various disciplines. We present a complex, multi-faceted annotation scheme of factuality that combines concepts from a variety of previous works. We developed the scheme for Hebrew, but we trust that it can be adapted to other languages. We also present a set of almost 5,000 sentences in the domain of parliamentary discourse that we manually annotated according to this scheme. We report on inter-annotator agreement, and experiment with various approaches to automatically predict (some features of) the scheme, in order to extend the annotation to a large corpus."
}