AI Bibliography
AI Bibliography |
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Dulhanty, C., Deglint, J. L., Daya, I. B., & Wong, A. (2019). Taking a stance on fake news: Towards automatic disinformation assessment via deep bidirectional transformer language models for stance detection. arXiv preprint arXiv:1911.11951. |
| Resource type: Journal Article BibTeX citation key: Dulhanty2019 View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, General, Military Science Subcategories: Big data, Cognitive Electronic Warfare, Cyber, Deep learning, Machine learning, Machine recognition, Q-learning Creators: Daya, Deglint, Dulhanty, Wong Publisher: Collection: arXiv preprint arXiv:1911.11951 |
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The exponential rise of social media and digital news in the past decade has had the unfortunate consequence of escalating what the United Nations has called a global topic of concern: the growing prevalence of disinformation. Given the complexity and time-consuming nature of combating disinformation through human assessment, one is motivated to explore harnessing AI solutions to automatically assess news articles for the presence of disinformation. A valuable first step towards automatic identification of disinformation is stance detection, where given a claim and a news article, the aim is to predict if the article agrees, disagrees, takes no position, or is unrelated to the claim. Existing approaches in literature have largely relied on hand-engineered features or shallow learned representations (e.g., word embeddings) to encode the claim-article pairs, which can limit the level of representational expressiveness needed to tackle the high complexity of disinformation identification. In this work, we explore the notion of harnessing large-scale deep bidirectional transformer language models for encoding claim-article pairs in an effort to construct state-of-the-art stance detection geared for identifying disinformation. Taking advantage of bidirectional cross-attention between claim-article pairs via pair encoding with self-attention, we construct a large-scale language model for stance detection by performing transfer learning on a RoBERTa deep bidirectional transformer language model, and were able to achieve state-of-the-art performance (weighted accuracy of 90.01%) on the Fake News Challenge Stage 1 (FNC-I) benchmark. These promising results serve as motivation for harnessing such large-scale language models as powerful building blocks for creating effective AI solutions to combat disinformation.
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