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.

该论文介绍了一个包含多种类型的文本解释的数据集 CICEROv2，提出了一系列的预训练目标来帮助下游任务。结果表明，该论文中的预训练目标对于预训练模型来说是有效的。

多视角的语境常识推理：一个新的数据集和任务

Multiview Contextual Commonsense Inference: A New Dataset and Task

Transformers have been showing near-human performance on a variety of tasks,
but they are not without their limitations. We discuss the issue of conflating
results of transformers that are instructed to do multiple tasks
simultaneously. In particular, we focus on the domain of commonsense reasoning
within story prose, which we call contextual commonsense inference (CCI). We
look at the GLUCOSE (Mostafazadeh et al. 2020) dataset and task for predicting
implicit commonsense inferences between story sentences. Since the GLUCOSE task
simultaneously generates sentences and predicts the CCI relation, there is a
conflation in the results. Is the model really measuring CCI or is its ability
to generate grammatical text carrying the results? In this paper, we introduce
the task contextual commonsense inference in sentence selection ($\rm{C {\small
IS}}^2$), a simplified task that avoids conflation by eliminating language
generation altogether. Our findings emphasize the necessity of future work to
disentangle language generation from the desired NLP tasks at hand.

通过 CIS2 任务避免语言生成误导，强调解开自然语言处理中的任务与语言生成之间相互混淆的必要性，并探讨了 Transformer 模型在通识推理任务中的表现限制。