Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modeling

TL;DR

This paper introduces a novel language modeling approach using masked language modeling techniques to improve dialogue grounding and task understanding in simulated environments, specifically within a Minecraft-based collective building task.

Contribution

The study presents a new method leveraging advanced language models to enhance multi-modal understanding and dialogue comprehension for embodied tasks in simulated environments.

Findings

Significant improvement over baseline models in task accuracy

Enhanced understanding of multi-modal inputs in dialogue scenarios

Potential for future research in embodied AI tasks

Abstract

Enhancing AI systems with efficient communication skills that align with human understanding is crucial for their effective assistance to human users. Proactive initiatives from the system side are needed to discern specific circumstances and interact aptly with users to solve these scenarios. In this research, we opt for a collective building assignment taken from the Minecraft dataset. Our proposed method employs language modeling to enhance task understanding through state-of-the-art (SOTA) methods using language models. These models focus on grounding multi-modal understandinging and task-oriented dialogue comprehension tasks. This focus aids in gaining insights into how well these models interpret and respond to a variety of inputs and tasks. Our experimental results provide compelling evidence of the superiority of our proposed method. This showcases a substantial improvement and points towards a promising direction for future research in this domain.