DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents (2022.findings-emnlp)
Copied to clipboard
Ziqiao Ma, Benjamin VanDerPloeg, Cristian-Paul Bara, Yidong Huang, Eui-In Kim, Felix Gervits, Matthew Marge, Joyce Chai
| Challenge: | Empowering autonomous driving agents with the ability to navigate in a continuous and dynamic environment is critical. |
| Approach: | They propose a novel interactive simulation platform that enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents. |
| Outcome: | The proposed platform enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents. |
Similar Papers
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)
Copied to clipboard
Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu
| Challenge: | Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands. |
| Approach: | They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans . |
| Outcome: | The proposed approach reduces human intervention and improves safety and efficiency. |
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
NDH-Full: Learning and Evaluating Navigational Agents on Full-Length Dialogue (2021.emnlp-main)
Copied to clipboard
| Challenge: | Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent’s ability to interact with humans for assistance and navigate based on natural language responses. |
| Approach: | They propose a vision-and-dialogue navigation task which evaluates the agent's ability to interact with humans and navigate based on natural language responses. |
| Outcome: | The proposed model performs well on the Navigation from Dialogue History task, but it is not evaluated by the primary metric Goal Progress. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
Copied to clipboard
Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
Spoken Conversational Agents with Large Language Models (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on the evolution of voice-native LLMs . it reviews the adaptation of text LLM to audio, cross-modal alignment, and joint speech–text training . |
| Approach: | This tutorial examines the evolution of voice-native LLMs in conversational agents . it compares cascaded and voice-based LLM systems to end-to-end retrieval-and vision-grounded systems . |
| Outcome: | This tutorial examines the evolution of voice-native LLMs . it compares the performance of voice assistants to current open-domain agents . |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
Copied to clipboard
| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2020.acl-main)
Copied to clipboard
| Challenge: | a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, and perceive and converse about images. |
| Approach: | They propose a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy . they use large dialogue datasets to multi-task and obtain state-of-the-art results . |
| Outcome: | The proposed model improves over a BERT pre-trained model on large dialogue datasets and provides state-of-the-art results on many of the tasks. |
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning (P18-1)
Copied to clipboard
| Challenge: | Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. |
| Approach: | They propose a framework that integrates planning for task-completion dialogue policy learning into a dialogue agent using a world model to mimic real user response and generate simulated experience. |
| Outcome: | The proposed framework integrates planning for task-completion dialogue policy learning with real user interaction and simulated user behavior. |
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation (2024.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that the model can be used to generate dialogues in new domains quickly. |
| Approach: | They propose to use LLMs to generate dialogue data to reduce dialogue collection and annotation costs. |
| Outcome: | The proposed model performs better than the baseline model trained on real data. |