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.

中文翻译：

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具有神经常识知识和符号逻辑规则的会话多跳推理

会话代理面临的挑战之一是他们无法识别用户命令的未声明假设，由于他们的常识，这对人类来说是一项微不足道的任务。在本文中，我们为会话代理提出了一种零样本常识推理系统，以尝试实现这一目标。我们的推理器从满足 if-(state), then-(action), 因为-(goal) 的通用模板的用户命令中发现了未声明的假设。我们的推理器使用最先进的基于转换器的生成常识知识库 (KB) 作为推理的背景知识来源。我们提出了一种新颖的迭代知识查询机制，从神经知识库中提取多跳推理链，该机制使用符号逻辑规则来显着减少搜索空间。类似于迄今为止收集的任何知识库，我们的常识知识库很容易丢失知识。因此，我们建议使用我们新颖的动态问题生成策略以对话方式从人类用户那里获取缺失的知识，该策略生成并向人类用户呈现上下文查询。我们通过对人类用户的用户研究来评估模型，与 SOTA 相比，该模型的成功率高出 35%。