The Anglocentric nature of scholarly communication has many implications, such as limiting publication, discoverability and access from other language communities (even for major languages); putting minoritized languages at risk in the academic domain; and excluding many from peer review. The OSCAIL project addresses these challenges by exploring how machine translation (MT) enhanced by large language model (LLM)–based technologies can support access to scientific knowledge. Outputs will include evaluation datasets, protocols and best practices for MT in scholarly communication, and a prototype integration of MT tools into Open Journal Systems, the world’s most widely used open-source scholarly publishing platform.

Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information.Recent research has shown that predicting sources’ reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking.In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web.We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F1 score=81.05).We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification.