One of the challenges faced by conversational agents is their inability to
identify unstated presumptions of their users' commands, a task trivial for
humans due to their common sense. In this paper, we propose a zero-shot
commonsense reasoning system for conversational agents in an attempt to achieve
this. Our reasoner uncovers unstated presumptions from user commands satisfying
a general template of if-(state), then-(action), because-(goal). Our reasoner
uses a state-of-the-art transformer-based generative commonsense knowledge base
(KB) as its source of background knowledge for reasoning. We propose a novel
and iterative knowledge query mechanism to extract multi-hop reasoning chains
from the neural KB which uses symbolic logic rules to significantly reduce the
search space. Similar to any KBs gathered to date, our commonsense KB is prone
to missing knowledge. Therefore, we propose to conversationally elicit the
missing knowledge from human users with our novel dynamic question generation
strategy, which generates and presents contextualized queries to human users.
We evaluate the model with a user study with human users that achieves a 35%
higher success rate compared to SOTA.

本文提出了一种用于对话代理的零样本通识推理系统，可以通过基于转换器的生成通识知识库找到用户命令中未明示的假设，同时提出了一种动态问题生成策略来从人类用户中提取缺失的知识，实验表明，相比现有技术，成功率提高了 35%。