论文标题
CICERO：用于对话中的上下文定量推断的数据集
CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
论文作者
论文摘要
本文通过上下文化的常识推理解决了对话推理的问题。我们策划Cicero，这是一个二元对话的数据集，具有五种类型的话语级推理：原因，随后的事件，先决条件，动机和情感反应。该数据集包含5,672个对话中的53,105个这些推论。我们使用此数据集来解决相关的生成和歧视任务：生成原因和后续事件；产生先决条件，动力和听众的情感反应；以及合理的替代方案的选择。我们的结果确定了以对话为中心的常识性知识数据集的价值。我们希望西塞罗（Cicero）能够为基于常识的对话推理开放新的研究途径。
This paper addresses the problem of dialogue reasoning with contextualized commonsense inference. We curate CICERO, a dataset of dyadic conversations with five types of utterance-level reasoning-based inferences: cause, subsequent event, prerequisite, motivation, and emotional reaction. The dataset contains 53,105 of such inferences from 5,672 dialogues. We use this dataset to solve relevant generative and discriminative tasks: generation of cause and subsequent event; generation of prerequisite, motivation, and listener's emotional reaction; and selection of plausible alternatives. Our results ascertain the value of such dialogue-centric commonsense knowledge datasets. It is our hope that CICERO will open new research avenues into commonsense-based dialogue reasoning.
