Proactive Suggestion Generation: Data and Methods for Stepwise Task Assistance
Proceedings of the 43 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) |
Published by ACM | Organized by ACM
Conversational systems such as digital assistants can help users perform many simple tasks upon request. Looking to the future, these systems will also need to fully support more complex, multi-step tasks (e.g., following cooking instructions), and help users complete those tasks, e.g., via useful and relevant suggestions made during the process. This paper takes the first step towards automatic generation of task-related suggestions. We introduce proactive suggestion generation as a novel task of natural language generation, in which a decision is made to inject a suggestion into an ongoing user dialog and one is then automatically generated. We propose two types of stepwise suggestions: multiple-choice response generation and text generation. We provide several models for each type of suggestion, including binary and multi-class classification, and text generation.