论文标题
部分可观测时空混沌系统的无模型预测
Dialog Acts for Task-Driven Embodied Agents
论文作者
论文摘要
体现的代理需要能够在自然语言中互动，理解任务描述，并提出适当的后续问题以获取必要的信息，以有效地成功完成各种用户的任务。在这项工作中，我们提出了一组对话框，用于对此类对话进行建模并注释教学数据集，其中包括3,000多个位置，以任务为导向的对话（总共包含39.5k个话语），并具有对话框ACT。 Teach-da是对Dialog ACT的第一个大型数据集注释，用于具体任务完成。此外，我们证明了在培训模型中使用该注释的数据集来标记给定话语的对话框行为，预测给定对话框历史记录的下一个响应的对话框行为，并使用对话框行为来指导代理商的非二元格行为。特别是，我们对对话记录任务的教学执行执行的实验，其中该模型预测了要在体现任务完成的环境中要执行的低级别动作的顺序，证明对话框行为可以将最终任务成功率提高2分，而无需系统，而没有对话框。
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. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. TEACh-DA is one of the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent's non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end task success rate by up to 2 points compared to the system without dialog acts.
