Humans can classify data of an unseen category by reasoning on its language explanations. This ability is owing to the compositional nature of language: we can combine previously seen attributes to describe the new category. For example, we might describe a sage thrasher as "it has a slim straight relatively short bill, yellow eyes and a long tail", so that others can use their knowledge of attributes "slim straight relatively short bill", "yellow eyes" and "long tail" to recognize a sage thrasher. Inspired by this observation, in this work we tackle zero-shot classification task by logically parsing and reasoning on natural language expla-nations. To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations). While previous methods usually regard textual information as implicit features, CLORE parses explanations into logical structures and then explicitly reasons along thess structures on the input to produce a classification score. Experimental results on explanation-based zero-shot classification benchmarks demonstrate that CLORE is superior to baselines, which we further show mainly comes from higher scores on tasks requiring more logical reasoning. We also demonstrate that our framework can be extended to zero-shot classification on visual modality. Alongside classification decisions, CLORE can provide the logical parsing and reasoning process as a clear form of rationale. Through empirical analysis we demonstrate that CLORE is also less affected by linguistic biases than baselines.

翻译：人类能够通过语言解释对未见类别的数据进行分类。这一能力源于语言的组合本质：我们可以将先前见过的属性组合起来描述新类别。例如，我们可以描述一只鼠鹬为"具有细长笔直且相对较短的喙、黄色眼睛和长尾巴"，使他人能运用对"细长笔直且相对较短的喙"、"黄色眼睛"和"长尾巴"等属性的知识来识别鼠鹬。受此启发，本文通过逻辑解析与推理自然语言解释来攻克零样本分类任务。为此，我们提出CLORE（基于解释逻辑推理的分类）框架。现有方法通常将文本信息视为隐式特征，而CLORE则将解释解析为逻辑结构，随后沿这些结构对输入进行显式推理以生成分类得分。在基于解释的零样本分类基准实验表明，CLORE优于基线方法，我们进一步证实这种优势主要源于其在需要更强逻辑推理的任务上取得更高得分。我们还证明该框架可拓展至视觉模态的零样本分类。除分类决策外，CLORE还能提供逻辑解析与推理过程作为清晰的解释形式。通过实证分析，我们证明CLORE受语言偏差影响的程度也小于基线方法。