Yucheng Li, Bo Dong, Frank Guerin, Chenghua Lin. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Yucheng Li, Yunhao Guo, Frank Guerin, Chenghua Lin. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Shun Wang, Yucheng Li, Chenghua Lin, Loic Barrault, Frank Guerin. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Yucheng Li, Chenghua Lin, Frank Guerin. Proceedings of the 15th International Conference on Natural Language Generation. 2022.

Artificial Intelligence systems cannot yet match human abilities to apply knowledge to situations that vary from what they have been programmed for, or trained for. In visual object recognition, methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Here a component of this type of inference, called ‘projection’, is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations, across a range of AI domains, such as vision, robotics, or language. Finally, the relevance of projection to tackling the commonsense knowledge problem is discussed.

Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin. Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2022.