在模拟环境中使用进一步掩码语言建模解决对话基础具身任务
Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modeling
AI总结：
本研究利用进一步掩码语言建模，在Minecraft模拟环境中解决对话基础具身任务，通过增强多模态理解与对话理解，显著提升了任务表现。
摘要：
增强人工智能系统与人类理解相一致的沟通能力，对于其有效协助人类用户至关重要。系统方面需要采取主动措施，以识别具体情况并与用户进行恰当互动来解决这些场景。在本研究中，我们选择了从Minecraft数据集中提取的集体建造任务。我们提出的方法采用语言建模，通过使用语言模型的最先进（SOTA）方法增强任务理解。这些模型专注于基础多模态理解和面向任务的对话理解任务。这一重点有助于深入了解这些模型如何解释和响应各种输入和任务。我们的实验结果提供了令人信服的证据，证明了我们提出方法的优越性。这展示了显著的改进，并为该领域的未来研究指明了有前景的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
