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.
翻译:社会常识推理要求理解文字,了解社会事件及其实际影响,以及常识推理技巧。在这项工作中,我们提出一个新的多头知识关注模式,将半结构的常识推理规则编码成半结构化的常识推理规则,并学会将其纳入以变压器为基础的推理单元。我们评估该模式在需要不同推理技巧的两项任务上的绩效:即:自然语言推断和反实际误判预测,作为一项新任务。我们表明,我们提议的模型在这两种推理任务中都比强势的先进模型(即罗贝塔)表现得更好。特别是,我们最了解的是,我们首先证明,一个学会进行反事实推理的模型有助于预测对引理任务的最佳解释。我们通过渗透知识,对模型的知识融入能力进行定性分析,来验证模型推理能力是否稳健。