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AG Audience

English

30 Minutes

Training and evaluation of automatic fact extraction and verification techniques require large amounts of annotated data which might not be available for low-resource languages. Furthermore, verifying many-hup (requires evidence from multiple sources) claims are more challenging and close to real-world queries than single-hup (evidence from one resource is enough). In this talk, I will present how we tackle these two challenges by gathering ParsFEVER: the first publicly available Farsi dataset for fact extraction and verification. The gathering procedure is inspired by the construction procedure of the standard English dataset for the task, i.e., FEVER, and improved for the case of low-resource languages. Claims are extracted from sentences that are carefully selected to be more informative. The dataset comprises nearly 23K manually-annotated claims. Over 65% of the claims in ParsFEVER are many-hop, making the dataset a challenging benchmark (only 13% of the claims in FEVER are many-hop). Also, despite having a smaller training set (around one-ninth of that in Fever), a model trained on ParsFEVER attains similar downstream performance, indicating the quality of the dataset. 

Jennifer Gerling

+49 681 9325 1801

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