Social Commonsense Reasoning requires understanding of text, knowledge about
social events and their pragmatic implications, as well as commonsense
reasoning skills. In this work we propose a novel multi-head knowledge
attention model that encodes semi-structured commonsense inference rules and
learns to incorporate them in a transformer-based reasoning cell. We assess the
model's performance on two tasks that require different reasoning skills:
Abductive Natural Language Inference and Counterfactual Invariance Prediction
as a new task. We show that our proposed model improves performance over strong
state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we
are, to the best of our knowledge, the first to demonstrate that a model that
learns to perform counterfactual reasoning helps predicting the best
explanation in an abductive reasoning task. We validate the robustness of the
model's reasoning capabilities by perturbing the knowledge and provide
qualitative analysis on the model's knowledge incorporation capabilities.

本文提出了一种新颖的多头知识注意模型，该模型对半结构化的常识推理规则进行编码，并学习将它们纳入基于 Transformer 的推理单元中。在推理任务中，我们评估了模型在需要不同推理技能的两个任务上的表现，即推断性自然语言推理和反事实不变性预测，并展示了我们提出的模型相对于强大的最先进模型（例如 RoBERTa）在两个推理任务中的表现提高，通过扰动知识，并对模型的知识纳入能力进行定性分析，我们验证了模型推理能力的稳健性。值得注意的是，我们在最佳解释方面展示了学习执行反事实推理的模型对推断性推理任务的帮助，这在我们所知道的情况下是首次演示的。