Event Date: Wednesday, 10 March, 2021 (15:00)
Teams: https://teams.microsoft.com/l/meetup-join/19%3ameeting_YmM1OGVmYWMtOTBkZi00MmZlLWE2ZGEtMjQyNTVkOWRjZjhh%40thread.v2/0?context=%7b%22Tid%22%3a%22c7456b31-a220-47f5-be52-473828670aa1%22%2c%22Oid%22%3a%22a53a14ac-fe44-4cb2-8a05-854c63a23450%22%7d
Speaker: Prof. Ekaterina Shutova (University of Amsterdam)
Title: Modelling the interplay of metaphor and emotion
Abstract: Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor plays a fundamental structural role in our cognition, helping us to organise and project knowledge. For example, when we say “a well-oiled political machine”, we view the concept of political system in terms of a mechanism and transfer inferences from the domain of mechanisms onto our reasoning about political processes. Much previous research on metaphor in linguistics and psychology suggests that metaphorical phrases tend to be more emotionally evocative than their literal counterparts. In this talk, I will present our recent work investigating the relationship between metaphor and emotion within a computational framework, by proposing the first joint model of these phenomena. We experiment with several multitask learning architectures for this purpose and demonstrate that metaphor identification and emotion prediction mutually benefit from joint learning, advancing the state of the art in both of these tasks.
Ekaterina Shutova is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, where she leads the Amsterdam Natural Language Understanding Lab. She received her PhD in Computer Science from the University of Cambridge, and then worked as a Research Scientist at the University of California, Berkeley and a Leverhulme Early Career Fellow at the University of Cambridge. Ekaterina’s research is in the area of natural language processing, with a specific focus on machine learning for natural language understanding tasks. Her current interests include few-shot learning and meta-learning, cognitively-motivated models of language, multilingual NLP and societal applications of NLP, such as hate speech and misinformation detection.