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Found 6 papers, 2 papers with code
Faithful Persona-based Conversational Dataset Generation with Large Language Models
Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement.
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
Probing Commonsense Explanation in Dialogue Response Generation
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
Human-like Time Series Summaries via Trend Utility Estimation
Our system finds patterns in time series data and ranks these patterns based on empirical observations of human behavior using utility estimation.
