Papers by Amanda Cercas Curry
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
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| Challenge: | Emotion analysis (EA) is a rapidly growing field in natural language processing . there is no consensus on scope, direction, or methods for EA . |
| Approach: | They review 154 relevant NLP papers on emotion analysis from the last decade . they ask: how are EA tasks defined in NLP? what are the most prominent emotion frameworks and which emotions are modeled? |
| Outcome: | The authors examine 154 relevant NLP papers on emotion analysis from the last decade . they find that there is no consensus on scope, direction, or methods . |
Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models (2024.findings-emnlp)
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| Challenge: | Previous work has shown that LLMs display biases in emotion attribution along gender lines. |
| Approach: | They examine how different religions are represented in LLMs by examining emotion attribution and cultural biases. |
| Outcome: | The findings highlight the need to address and rectify these biases in LLMs. |
Computer says “No”: The Case Against Empathetic Conversational AI (2023.findings-acl)
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| Challenge: | Recent work in conversational AI has focused on responding empathetically to users' emotions without a real basis. |
| Approach: | They argue that we must carefully consider whether and how to respond to users' emotions. |
| Outcome: | Recent work in conversational AI has focused on responding empathetically to users' emotions without a real basis. |
Impoverished Language Technology: The Lack of (Social) Class in NLP (2024.lrec-main)
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| Challenge: | Existing work on socio-demographic factors has focused on how much a person's socioeconomic status affects their language production and perception. |
| Approach: | They propose to include socio-economic class in future natural language processing (NLP) research aimed at understanding relationships between socio-demographic factors and language production and perception. |
| Outcome: | The proposed definition of class can be operationalised by NLP researchers and argue for including socio-economic class in future language technologies. |
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021.emnlp-main)
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| Challenge: | Existing studies on abusive language towards conversational AI systems are not conclusive as they are not performed with live systems nor with real users due to the lack of reliable abuse detection tools. |
| Approach: | They propose to use a convAI dataset to account for the complexity of the task and to bench-mark existing models against this data. |
| Outcome: | The proposed model shows that abuse distribution is different compared to other datasets, with sexual tinted aggression towards the virtual persona of the systems. |
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution (2024.acl-long)
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| Challenge: | Large language models reflect societal norms and biases, especially about gender. |
| Approach: | They propose to use large language models to examine gendered emotion attribution in five state-of-the-art LLMs to investigate whether emotions are genderes and whether they are influenced by societal stereotypes. |
| Outcome: | The proposed models exhibit gendered emotions, influenced by gender stereotypes, and the results are consistent with established research in psychology and gender studies. |