  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.
