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:RANLP, author = {Goldin, Gili and Wigderson, Shira and Rabinovich, Ella and Wintner, Shuly}, title = {An Annotation Scheme for Factuality and Its Application to Parliamentary Proceedings}, booktitle = {Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI era}, month = {September}, year = {2025}, address = {Varna, Bulgaria}, publisher = {INCOMA Ltd., Shoumen, Bulgaria}, 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.}, url = {https://aclanthology.org/2025.ranlp-1.49} }