Multiview Contextual Commonsense Inference: A New Dataset and Task
This work addresses the need for better commonsense reasoning in AI dialogue systems, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of contextual commonsense inference in dialogues by creating CICEROv2, a dataset with 8,351 instances from 2,379 dialogues, and showed it has more semantic diversity than existing datasets. They proposed pre-training objectives like concept denoising and utterance sorting, which effectively adapted T5-Large for this task.
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.