| Bonnie Dorr, Ph.D., and her talented team of students have had two research papers accepted to the prestigious Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), set to take place this year in Albuquerque, New Mexico.
The first paper, titled “FIDELITY: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding,” showcases a novel approach to interpretable machine learning and language understanding. Dr. Dorr co-authored this work with students Divyansh Singh, Brodie Mather, and Justin Ho, who contributed to developing and evaluating this cutting-edge framework.
The second accepted paper, “DETQUS: Decomposition-Enhanced Transformers for Query-focused Summarization,” presents an innovative method for improving transformer models in summarization tasks, specifically tailored to query-focused use cases. This work was made possible through the collaborative efforts of students Yasir Khan, Xinlei Wu, Sangpil Youm, Justin Ho, Aryaan Shaikh, Jairo Garciga, and Rohan Sharma.
Both projects highlight the groundbreaking research being conducted under Dr. Dorr’s mentorship and underscore the strength of interdisciplinary collaboration in advancing the field of computational linguistics. (Read More) |
