Inspired by studies on the overwhelming presence of experience-sharing in
human-human conversations, Emora, the social chatbot developed by Emory
University, aims to bring such experience-focused interaction to the current
field of conversational AI. The traditional approach of information-sharing
topic handlers is balanced with a focus on opinion-oriented exchanges that
Emora delivers, and new conversational abilities are developed that support
dialogues that consist of a collaborative understanding and learning process of
the partner's life experiences. We present a curated dialogue system that
leverages highly expressive natural language templates, powerful intent
classification, and ontology resources to provide an engaging and interesting
conversational experience to every user.

Emora 是一款基于对人际交谈中体验分享的研究而开发的社交聊天机器人，它通过高度表达自然语言模板、强大的意图分类和本体资源来提供有趣和愉悦的对话体验，并支持以合作的方式理解和学习对方生活经历的对话能力。

Emora: 一个关心你的好奇社交聊天机器人

Emora: An Inquisitive Social Chatbot Who Cares For You

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。