Publication date: 8 de July, 2024

From Ambiguity to Clarity: Navigating Uncertainty in Human-Machine Conversations

This talk delves into the intricacies of uncertainty in human-machine dialogue, mainly focusing on the challenges and solutions related to ambiguities arising from impoverished contextual representations. We examine how linguistically informed context representations can mitigate data-related uncertainty in a deployed dialogue system similar to Alexa. We acknowledge that certain types of data-related uncertainty are unavoidable and investigate the capabilities of modern billion-scale language models in representing this form of uncertainty in conversations. Shifting our focus to epistemic uncertainty arising from misaligned background knowledge between humans and machines, we explore strategies for quantifying and reducing this form of uncertainty. Our discussion encompasses various facets of human-machine convergence, including lexical diversity, question generation, fairness, and pragmatics. By leveraging machine learning theory and cognitive science insights, we aim to quantify epistemic uncertainty and propose algorithms that improve grounding between humans and machines. This exploration sheds light on the theoretical underpinnings of uncertainty in dialogue systems and offers practical solutions for improving human-machine communication.


Malihe Alikhani (Northeastern University),

Date 15/07/2024 11:00 am
Location DI Seminars Room and Zoom
Host Bio Malihe Alikhani is an assistant professor of AI and social justice at the Khoury School of Computer Science, Northeastern University. She is affiliated with the Northeastern Ethics Institute as well of the Institute for Experiential AI. Her research interests center on using representations of communicative structure, machine learning, and cognitive science to design equitable and inclusive NLP systems for critical applications such as education, health, and social justice. She has designed several models for sign language understanding generation, dialogue systems for deaf and hard of hearing and AI systems for evaluation of speech impairment. Her work has received multiple best paper awards at ACL 2021, UAI2022, INLG2021, UMAP2022, and EMNLP 2023 and has been supported by DARPA, NIH, CDC, Google, and Amazon.