A neural network can identify Wikipedia references that are unlikely to support an article’s claims—and scour the web for better sources
Wikipedia lives and dies by its references, the links to sources that back up information in the online encyclopaedia. But sometimes, those references are flawed — pointing to broken websites, erroneous information or non-reputable sources.
Fabio Petroni at London-based company Samaya AI and his colleagues developed a neural-network-powered system called SIDE, which analyses whether Wikipedia references support the claims they’re associated with, and suggests better alternatives for those that don’t. AI filter SIDE is trained to recognize good references using existing featured Wikipedia articles, which are promoted on the site and receive a lot of attention from editors and moderators.
To put the system to the test, Petroni and his colleagues used SIDE to suggest references for featured Wikipedia articles that it had not seen before. In nearly 50% of cases, SIDE’s top choice for a reference was already cited in the article. For the others, it found alternative references.
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