Events

Making Building NLP Models More Accessible

Speaker:

Dr. Michael A. Hedderich
Postdoctoral researcher, Cornell University

Date:

May 15, 2023; 17:00–18:00

Location:

Akademiestr. 7, room 218A (meeting room)

Abstract:

AI and NLP are entering more and more disciplines and applications. Individuals, research groups, and organizations who are interested in AI are limited in what they can do, however, due to reasons such as lack of labeled data, complexity of the model-building process, missing AI literacy, and applications that do not apply to their use cases. In this talk, I'll present two projects that aim at lowering the entry barriers to model development. The first part will cover a study on using low-resource techniques for under-resourced African languages. I'll discuss the lessons we learned when evaluating in a realistic environment and the importance of integrating the human factor in this evaluation. In the second part of the talk, I'll present Premise, a tool that explains where an NLP classifier fails. Based on the minimum description length principle, it provides a set of robust and global explanations of a model's behavior. For VQA and NER, we identify the issues different blackbox classifiers have and we also show how these insights can be used to improve models.
Portrait of Michael A. Hedderich

Bio:

Michael A. Hedderich is a postdoctoral researcher at Cornell University, working with Qian Yang at the intersection of NLP and AI with HCI. Having a background in both NLP and ML as well as HCI methodology, he is interested in developing new foundational technology as well as building bridges from AI to other interested fields. His collaborations span a wide range of disciplines including archaeology, education, interaction design, participatory design, and biomedicine. Before joining Cornell, Michael obtained his PhD in ML and NLP at Saarland University, Germany, with Dietrich Klakow and was then part of Antti Oulasvirta's HCI group at Aalto University, Finland. Past research affiliations also include Rutgers University, Disney Research Studios, and Amazon. → Website

The Search for Emotions, Creativity, and Fairness in Language

Speaker:

Dr. Saif M. Mohammad (he, him, his)
Senior Research Scientist, National Research Council Canada

Date:

May 8, 2023; 9:00–10:00

Location:

LMU main building (Geschwister-Scholl-Platz 1), room A 015

Abstract:

Emotions are central to human experience, creativity, and behavior. They are crucial for organizing meaning and reasoning about the world we live in. They are ubiquitous and everyday, yet complex and nuanced. In this talk, I will describe our work on the search for emotions in language — by humans (through data annotation projects) and by machines (in automatic emotion and sentiment analysis systems). I will outline ways in which emotions can be represented, challenges in obtaining reliable annotations, and approaches that lead to high-quality annotations and useful sentiment analysis systems. I will discuss wide-ranging applications of emotion detection in natural language processing, psychology, social sciences, digital humanities, and computational creativity. Along the way, I will discuss various ethical considerations involved in emotion recognition and sentiment analysis — the often unsaid assumptions and the real-world implications of our choices.
Portrait of Saif Mohammad

Bio:

Dr. Saif M. Mohammad is a Senior Research Scientist at the National Research Council Canada (NRC). He received his Ph.D. in Computer Science from the University of Toronto. Before joining NRC, he was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park. His research interests are in Natural Language Processing (NLP), especially Lexical Semantics, Emotions and Language, Computational Creativity, AI Ethics, NLP for psychology, and Computational Social Science. He is currently an associate editor for Computational Linguistics, JAIR, and TACL, and Senior Area Chair for ACL Rolling Review. His word--emotion resources, such as the NRC Emotion Lexicon and VAD Lexicon, are widely used for analyzing emotions in text. His work has garnered media attention, including articles in Time, SlashDot, LiveScience, io9, The Physics arXiv Blog, PC World, and Popular Science. → Website