Artificial Intelligence (AI) systems are playing an increasingly important role in our daily lives, and it will also have significant impact to our future. One important aspect of this AI-powered future is that, we will be able to interact with these AI systems comfortably and confidently. To work towards such a future, we need Natural Language Processing (NLP) systems that can understand human language efficiently and robustly. In this talk, we present some recent works in this direction, including i) efficient Question Answering (QA) with Adaptive Computation, ii) a retrieval-based QA system with generate Probably-Asked Questions (winner solution of Neurips2020 EfficientQA competition), and iii) a data generation method to create debiased datasets to improve robustness NLI models. We end the talk with a discussion of efficient methods to incorporate large scale knowledge into NLP models.
Speaker: Mr Yuxiang WU
Date: 17 August 2022 (Wednesday)
Time: 9:00am – 10:00am
Poster: Click here
Mr Yuxiang WU is a final-year PhD student in Computer Science at University College London, supervised by Prof. Sebastian Riedel and Prof. Pontus Stenetorp. His research interest lies at the intersection of Natural Language Processing (NLP) and Deep Learning, and his current focus is on Question Answering, Data Generation, and Knowledgeable Pretrained Language Models. He published nine papers in top-tier AI/NLP conferences and journals (ACL, EMNLP, AAAI, IJCAI, TACL, etc.), and his work received more than 1069 citations (Google Scholar). He also won AKBC2020 Best Paper Award, Neurips 2020 EfficientQA Competition Champion in two tracks, and is a regular PC member of ACL/EMNLP/NAACL/AAAI/EACL conferences and various workshops.