Following complex instructions in conversational assistants can be quite daunting due to the shorter attention and memory spans when compared to reading the same instructions. Hence, when conversational assistants walk users through the steps of complex tasks, there is a need to structure the task into manageable pieces of information of the right length and complexity. In this paper, we tackle the recipes domain and convert reading structured instructions into conversational structured ones. We annotated the structure of instructions according to a conversational scenario, which provided insights into what is expected in this setting. To computationally model the conversational step's characteristics, we tested various Transformer-based architectures, showing that a token-based approach delivers the best results. A further user study showed that users tend to favor steps of manageable complexity and length, and that the proposed methodology can improve the original web-based instructional text. Specifically, 86% of the evaluated tasks were improved from a conversational suitability point of view.

翻译：在对话助手中，由于用户处理复杂指令时的注意力和记忆跨度相较于阅读相同指令更为有限，因此遵循复杂指令可能颇具挑战性。当对话助手引导用户逐步完成复杂任务时，有必要将任务划分为长度和复杂度适中的信息片段。本文聚焦食谱领域，将结构化阅读指令转化为对话式结构化指令。我们根据对话场景对指令结构进行了标注，从而揭示了该场景下的预期特征。为计算建模对话步骤的特征，我们测试了多种基于Transformer的架构，结果表明基于标记的方法效果最佳。进一步的用户研究显示，用户倾向于复杂度与长度适中的步骤，且所提方法能够改进原始基于网页的教学文本。具体而言，从对话适宜性角度评估，86%的任务得到了改进。