Supervised learning has traditionally focused on inductive learning by
observing labeled examples of a task. In contrast, humans have the ability to
learn new concepts from language. Here, we explore training zero-shot
classifiers for structured data purely from language. For this, we introduce
CLUES, a benchmark for Classifier Learning Using natural language ExplanationS,
consisting of a range of classification tasks over structured data along with
natural language supervision in the form of explanations. CLUES consists of 36
real-world and 144 synthetic classification tasks. It contains crowdsourced
explanations describing real-world tasks from multiple teachers and
programmatically generated explanations for the synthetic tasks. To model the
influence of explanations in classifying an example, we develop ExEnt, an
entailment-based model that learns classifiers using explanations. ExEnt
generalizes up to 18% better (relative) on novel tasks than a baseline that
does not use explanations. We delineate key challenges for automated learning
from explanations, addressing which can lead to progress on CLUES in the
future. Code and datasets are available at: this https URL.

研究使用自然语言来训练从语言中零样本学习结构化数据的分类器，并提出一个评估标准 CLUES，其包含 36 个现实世界和 144 个合成分类任务的自然语言监督。为实现此目的，开发了 ExEnt，一种基于蕴含的模型，通过学习解释来学习分类器。