Research
8 peer-reviewed publications, with first-author papers at EMNLP and COLING.
Interpretable Topic Modeling of Spontaneous Speech in Depression Using Large Language Models
We identify interpretable topics in spontaneous speech across four cohorts and examine how they relate to depression, to the questions that elicit them, and to demographics.
Formalizing Style in Personal Narratives
We model narrative style as recurring patterns in how people describe experiences, found with language models and pattern mining, and linked to psychological observations.
Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild
We compare modeling visual and acoustic features directly with translating them into text for a language model. We placed third in the ABAW compound expression challenge.
Improving Language Models for Emotion Analysis: Insights from Cognitive Science
We use theories of emotion and communication to identify what emotion-label prediction overlooks and propose directions for annotation, modeling, and evaluation.
Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives
Supervised sequence-to-sequence models outperform in-context learning with a larger model at detecting characters and emotions. I released their DreamBank annotations.
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation
We introduce French autobiographical narratives annotated for behavior, feeling, thinking, and context, and examine how these components contribute to emotion recognition.