论文标题
拉斯克：通过量词建模和课程学习从解释中改进了零拍的分类
LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning
论文作者
论文摘要
人类智能的标志是纯粹是从语言中学习新概念的能力。最近的几种方法通过自然语言监督探索了培训机学习模型。但是，这些方法在利用语言量词（例如“总是”或“很少”）并模仿人类中的人类中缺乏这些方法。在这里，我们提出了拉斯克语，一种可以通过使用三种新策略从语言解释中学习零拍的分类器的方法 - （1）在解释中对语言量化词的语义进行建模（包括诸如“始终'>'>“可能”），（2）使用基于注意的机构培训的多个解释中的多个解释中的信息，并通过（3）通过（3）通过（3）通过（3）通过（3）通过（3）通过（3）通过（3）进行模型来凝固。通过这些策略，拉斯克的表现优于先前的工作，显示出多达7％的绝对增益来推广到看不见的现实世界分类任务。
A hallmark of human intelligence is the ability to learn new concepts purely from language. Several recent approaches have explored training machine learning models via natural language supervision. However, these approaches fall short in leveraging linguistic quantifiers (such as 'always' or 'rarely') and mimicking humans in compositionally learning complex tasks. Here, we present LaSQuE, a method that can learn zero-shot classifiers from language explanations by using three new strategies - (1) modeling the semantics of linguistic quantifiers in explanations (including exploiting ordinal strength relationships, such as 'always' > 'likely'), (2) aggregating information from multiple explanations using an attention-based mechanism, and (3) model training via curriculum learning. With these strategies, LaSQuE outperforms prior work, showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks.
