4 papers
Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition
Minxue Niu, Emily Mower Provost
The ability to handle various emotion labels without dedicated training is crucial for building adaptable Emotion Recognition (ER) systems. Conventional ER models rely on training…
Rethinking Emotion Annotations in the Era of Large Language Models
Minxue Niu, Yara El-Tawil, Amrit Romana +1
Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, se…
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs
Minxue Niu, Mimansa Jaiswal, Emily Mower Provost
Training emotion recognition models has relied heavily on human annotated data, which present diversity, quality, and cost challenges. In this paper, we explore the potential of La…
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models
Matthew Perez, Aneesha Sampath, Minxue Niu +1
Aphasia is a language disorder that can lead to speech errors known as paraphasias, which involve the misuse, substitution, or invention of words. Automatic paraphasia detection ca…