Search Results for author:
Found 7 papers, 5 papers with code
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation
In this paper, we aim to control the protagonist’s persona in story generation, i. e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality through a coherent event sequence.
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Endowing the protagonist with a specific personality is essential for writing an engaging story.
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
AugESC: Large-scale Data Augmentation for Emotional Support Conversation with Pre-trained Language Models
Lastly, we demonstrate with interactive evaluation that AugESC can further enhance dialog models tuned on ESConv to handle various conversation topics and to provide significantly more effective emotional support.
Robustness Testing of Language Understanding in Task-Oriented Dialog
Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution.
