A new benchmark dataset and two tasks let researchers test whether AI systems can judge if a news image's recorded location and date match the article, with current chatbots scoring 64-81% on location but 42-58% on date.
Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task
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abstract
In this paper, we report improved results of the Fake News Challenge Stage 1 (FNC-1) stance detection task. This gain in performance is due to the generalization power of large language models based on Transformer architecture, invented, trained and publicly released over the last two years. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.
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Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
A new benchmark dataset and two tasks let researchers test whether AI systems can judge if a news image's recorded location and date match the article, with current chatbots scoring 64-81% on location but 42-58% on date.