Both humans and machines learn the meaning of unknown words through contextual information in a sentence, but not all contexts are equally helpful for learning. We introduce an effective method for capturing the level of contextual informativeness with respect to a given target word. Our study makes three main contributions. First, we develop models for estimating contextual informativeness, focusing on the instructional aspect of sentences. Our attention-based approach using pre-trained embeddings demonstrates state-of-the-art performance on our single-context dataset and an existing multi-sentence context dataset. Second, we show how our model identifies key contextual elements in a sentence that are likely to contribute most to a reader's understanding of the target word. Third, we examine how our contextual informativeness model, originally developed for vocabulary learning applications for students, can be used for developing better training curricula for word embedding models in batch learning and few-shot machine learning settings. We believe our results open new possibilities for applications that support language learning for both human and machine learners.

翻译：人类和机器都通过句子中的上下文信息来学习未知词汇的含义，但并非所有上下文对学习都有同等帮助。我们提出了一种有效方法，用于捕捉特定目标词的上下文信息量水平。本研究主要包含三项贡献。首先，我们开发了评估上下文信息量的模型，重点关注句子的教学属性。我们基于预训练嵌入的注意力方法，在单上下文数据集和现有的多句子上下文数据集上均展现出最优性能。其次，我们展示了模型如何识别句子中可能对读者理解目标词贡献最大的关键上下文元素。第三，我们探讨了最初专为学生词汇学习应用开发的上下文信息量模型，如何被用于在批量学习和少样本机器学习场景中，为词嵌入模型开发更优的训练课程。我们相信，这些成果为支持人类与机器学习者语言学习的应用开辟了新的可能性。