Solving Dialogue Grounding Embodied Task in a Simulated Environment
  using Further Masked Language Modeling
Weijie Jack Zhang
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.