Grounded Complex Task Segmentation for Conversational Assistants
Rafael Ferreira David Semedo João Magalhães
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.