In order for conversational AI systems to hold more natural and broad-ranging
conversations, they will require much more commonsense, including the ability
to identify unstated presumptions of their conversational partners. For
example, in the command "If it snows at night then wake me up early because I
don't want to be late for work" the speaker relies on commonsense reasoning of
the listener to infer the implicit presumption that they wish to be woken only
if it snows enough to cause traffic slowdowns. We consider here the problem of
understanding such imprecisely stated natural language commands given in the
form of "if-(state), then-(action), because-(goal)" statements. More precisely,
we consider the problem of identifying the unstated presumptions of the speaker
that allow the requested action to achieve the desired goal from the given
state (perhaps elaborated by making the implicit presumptions explicit). We
release a benchmark data set for this task, collected from humans and annotated
with commonsense presumptions. We present a neuro-symbolic theorem prover that
extracts multi-hop reasoning chains, and apply it to this problem. Furthermore,
to accommodate the reality that current AI commonsense systems lack full
coverage, we also present an interactive conversational framework built on our
neuro-symbolic system, that conversationally evokes commonsense knowledge from
humans to complete its reasoning chains.

为了能让对话式 AI 系统拥有更自然和广泛的对话，需要更多常识，包括识别对话伙伴的不明说的假设。本研究提出了一个基于神经符号定理证明器的多跳推理链提取方法来理解这类不精确陈述的自然语言命令，并提供一个交互式对话框架来从人类中挖掘常识知识来完善其推理链。