Detecting quoted claims and claim speakers in news articles using transformer-based language models

Abstract

Quotations in news articles have been suggested as a source of subjective or biased information. This work deals with automatically detecting the quoted claims and speakers of the claims in news articles. We suggest that quoted claims in news articles appear in predictable, but non-trivial ways. We annotate 33 articles for their quoted claims, speakers, and relations between claims and speakers. A dataset created with the presented annotation scheme is used to experiment with i) claim identification, ii) speaker identification, iii) claim-speaker relation identification, iv) claim group identification, and v) speaker group identification. The annotation and experimental results suggest that the contextual information must be taken into account in order to annotate and predict quoted claims and their speakers.