Search Results for author:
Found 15 papers, 4 papers with code
Dialog Acts for Task Driven Embodied Agents
Embodied agents need to be able to interact in natural language – understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users.
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
Multimodal Contextualized Plan Prediction for Embodied Task Completion
Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks.
KILM: Knowledge Injection into Encoder-Decoder Language Models
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.
Dialog Acts for Task-Driven Embodied Agents
Embodied agents need to be able to interact in natural language understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users.
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets
Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes.
Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation
We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.
TEACh: Task-driven Embodied Agents that Chat
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
Generative Conversational Networks
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
Dialog as a Vehicle for Lifelong Learning
Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained "chit chat" conversations.
Dialog Policy Learning for Joint Clarification and Active Learning Queries
Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training.
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Natural language understanding for robotics can require substantial domain- and platform-specific engineering.
Learning a Policy for Opportunistic Active Learning
Active learning identifies data points to label that are expected to be the most useful in improving a supervised model.
Integrated Learning of Dialog Strategies and Semantic Parsing
Natural language understanding and dialog management are two integral components of interactive dialog systems.
