论文标题
多视图上下文分解推理：一个新的数据集和任务
Multiview Contextual Commonsense Inference: A New Dataset and Task
论文作者
论文摘要
上下文常识推论是在二元对话中产生围绕事件的各种解释的任务，包括原因，动机，情感反应等。产生连贯和非平凡的解释需要了解对话的结构以及事件在背景下的基础。在这项工作中，我们创建了CICEROV2，该数据集由2,379个对话中的8,351个实例组成，其中包含每个上下文常识性推理问题的多个人工编写的答案，代表了关于原因，随后的事件，动机和情感反应的一种解释。我们表明，Cicerov2中的推论在语义上比其他上下文常识推理数据集更为多样。为了解决推理任务，我们提出了一系列预训练目标的集合，包括概念denoing和话语分类，以准备下游上下文中上下文常识推理任务的预训练模型。我们的结果表明，提议的预训练目标有效地适应了预先常识推理任务的预训练的T5大型模型。
Contextual commonsense inference is the task of generating various types of explanations around the events in a dyadic dialogue, including cause, motivation, emotional reaction, and others. Producing a coherent and non-trivial explanation requires awareness of the dialogue's structure and of how an event is grounded in the context. In this work, we create CICEROv2, a dataset consisting of 8,351 instances from 2,379 dialogues, containing multiple human-written answers for each contextual commonsense inference question, representing a type of explanation on cause, subsequent event, motivation, and emotional reaction. We show that the inferences in CICEROv2 are more semantically diverse than other contextual commonsense inference datasets. To solve the inference task, we propose a collection of pre-training objectives, including concept denoising and utterance sorting to prepare a pre-trained model for the downstream contextual commonsense inference task. Our results show that the proposed pre-training objectives are effective at adapting the pre-trained T5-Large model for the contextual commonsense inference task.
