Humans can classify an unseen category by reasoning on its language explanations. This ability is owing to the compositional nature of language: we can combine previously seen concepts to describe the new category. For example, we might describe mavens as "a kind of large birds with black feathers", so that others can use their knowledge of concepts "large birds" and "black feathers" to recognize a maven. Inspired by this observation, in this work we tackle zero-shot classification task by logically parsing and reasoning on natural language explanations. 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 the explanations into logical structure the and then reasons along this structure on the input to produce a classification score. Experimental results on explanation-based zero-shot classification benchmarks demonstrate that CLORE is superior to baselines, mainly because it performs better on tasks requiring more logical reasoning. Alongside classification decisions, CLORE can provide the logical parsing and reasoning process as a form of rationale. Through empirical analysis we demonstrate that CLORE is also less affected by linguistic biases than baselines.
翻译:人类可以通过对其语言解释的推理来分类一个看不见的类别。 这种能力是由于语言的构成性质: 我们可以将先前所见的概念结合起来来描述新的类别。 例如, 我们可以将 Maven 描述为“ 一种黑色羽毛的大型鸟类 ”, 这样其他人可以使用他们对“ 大鸟” 和“黑羽毛” 概念的知识来识别一个修饰。 受这一观察的启发, 我们在此工作中通过逻辑解析和自然语言解释的推理, 解决零分分类任务。 为此, 我们提议框架 ClORE (通过解释解释的解析分类) 。 虽然以前的方法通常将文本信息视为隐含的特征, 但 CLOR 将解释描述描述描述作为逻辑结构, 从而得出一个分类分数。 解释性零发分类基准的实验结果表明, CLOR 优于基线, 主要是因为它在需要更符合逻辑推理的任务上表现得更好。 除了分类决定之外, CLORE 可以提供逻辑的分解和推理过程, 作为一种解释性分析形式。 我们通过实验性分析来证明, CLORE 也较少受到语言上的判断性基线的影响。