Run Chen

Run Chen, Jun Shin, Julia Hirschberg

Previous research has shown that humans are more receptive towards language models that that exhibit empathetic behavior. While empathy is essential for developing helpful dialogue agents, very few large corpora containing empathetic dialogues are available for fine-tune LLMs. The few existing corpora have largely relied on crowdsourcing to simulate empathetic conversations, a process that is expensive, time-consuming, and not scalable to larger datasets. We propose a data generation framework for developing SYNTHEMPATHY, a large corpus containing 105k empathetic responses to real-life situations compiled through LLM generation. A base Mistral 7B model fine-tuned on our SYNTHEMPATHY corpus exhibits an increase in the average empathy score.

Ziwei Gong, Pengyuan Shi, Kaan Donbekci et al.

Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised learning to improve SER in low-resource settings. Specifically, we investigate contrastive learning (CL) and Bootstrap Your Own Latent (BYOL) as self-supervised approaches to enhance cross-lingual generalization. Our methods achieve notable F1 score improvements of 10.6% in Urdu, 15.2% in German, and 13.9% in Bangla, demonstrating their effectiveness in LRLs. Additionally, we analyze model behavior to provide insights on key factors influencing performance across languages, and also highlighting challenges in low-resource SER. This work provides a foundation for developing more inclusive, explainable, and robust emotion recognition systems for underrepresented languages.

Maya Srikanth, Run Chen, Julia Hirschberg

Multimodal models play a key role in empathy detection, but their performance can suffer when modalities provide conflicting cues. To understand these failures, we examine cases where unimodal and multimodal predictions diverge. Using fine-tuned models for text, audio, and video, along with a gated fusion model, we find that such disagreements often reflect underlying ambiguity, as evidenced by annotator uncertainty. Our analysis shows that dominant signals in one modality can mislead fusion when unsupported by others. We also observe that humans, like models, do not consistently benefit from multimodal input. These insights position disagreement as a useful diagnostic signal for identifying challenging examples and improving empathy system robustness.