论文标题
AFEC：在随意对话中捕获社会情报的知识图
AFEC: A Knowledge Graph Capturing Social Intelligence in Casual Conversations
论文作者
论文摘要
本文介绍了AFEC，这是一个基于人们日常休闲对话的自动策划知识图。该图中捕获的知识具有使对话系统了解人们如何在社交对话中提供认可，安慰和广泛的善解人意反应的潜力。为了使知识具有全面和有意义，我们策划了R/CasualConversation subreddit的大规模语料库。在进行所有对话的前两个转弯之后，我们获得了134K扬声器节点和666K侦听器节点。为了证明聊天机器人如何在社交环境中交谈，我们构建了基于检索的聊天机器人，并将其与现有的善解人意对话模型进行了比较。实验表明，我们的模型能够产生更多样化的响应（人类评估中的多样性得分至少提高15％），同时在响应质量方面仍然超过了四个基线中的两个。
This paper introduces AFEC, an automatically curated knowledge graph based on people's day-to-day casual conversations. The knowledge captured in this graph bears potential for conversational systems to understand how people offer acknowledgement, consoling, and a wide range of empathetic responses in social conversations. For this body of knowledge to be comprehensive and meaningful, we curated a large-scale corpus from the r/CasualConversation SubReddit. After taking the first two turns of all conversations, we obtained 134K speaker nodes and 666K listener nodes. To demonstrate how a chatbot can converse in social settings, we built a retrieval-based chatbot and compared it with existing empathetic dialog models. Experiments show that our model is capable of generating much more diverse responses (at least 15% higher diversity scores in human evaluation), while still outperforming two out of the four baselines in terms of response quality.
