Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges

2020/06/10 by Maxine Eskénazi, Tiancheng Zhao, Eskenazi, Maxine +1
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multi-Agent Systems and Negotiation #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2006.06026

openalex publication_date 2020/06/10 · openalex created_date 2020/06/19 · openalex updated_date 2026/07/28

Abstract

This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National Science Foundation. It took place in Pittsburgh Pennsylvania on October 24 and 25, 2019 and was sponsored by National Science Foundation Grant Number IIS-1934222. Any opinions, findings and conclusions or future directions expressed in this document are those of the authors and do not necessarily reflect the views of the National Science Foundation. The 27 participants presented their individual research interests and their personal research goals. In the breakout sessions that followed, the participants defined the main research areas within the domain of intelligent agents and they discussed the major future directions that the research in each area of this domain should take

Citations

• A Neural Conversational Model
• A Persona-Based Neural Conversation Model
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
• Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
• DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
• Rasa: Open Source Language Understanding and Dialogue Management
• Personalizing Dialogue Agents: I have a dog, do you have pets too?
• Datasheets for Datasets
• Zero-Shot Dialog Generation with Cross-Domain Latent Actions
• A Multimodal LSTM for Predicting Listener Empathic Responses Over Time
• Leveraging User Engagement Signals For Entity Labeling in a Virtual\n Assistant
• Adversarial Learning for Neural Dialogue Generation
• Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
• deltaBLEU: A Discriminative Metric for Generation Tasks with\n Intrinsically Diverse Targets
• QuAC : Question Answering in Context
• MoEL: Mixture of Empathetic Listeners

Related

• Envisioning National Resources for Artificial Intelligence Research: NSF\n Workshop Report
• Report on NSF Workshop on Science of Safe AI
• AI Agent Governance: A Field Guide
• Report of 2017 NSF Workshop on Multimedia Challenges, Opportunities and Research Roadmaps
• Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation