Advances in Natural Language Processing (NLP) over the past decade have largely been driven by the scale of data and computation used to train large neural network-based models. However, these techniques are inapplicable to the vast majority of the world's languages, which lack the vast digitized text datasets available for English and a few other…

Second language learners constitute a significant and expanding portion of the global population and there is a growing demand for tools that facilitate language learning and instruction across various levels and in different countries. The development of large language models (LLMs) has brought about a significant impact on the domains of natural…

Naturalistic language learning is contextually grounded. When people learn their first (L1) and often their second (L2) language, they do so in various contexts. In this dissertation I examine the effect of various contexts on language development. Part 1 describes the effects of textual, linguistic context in reading. I employed an eye-tracking…

Natural language is highly complex and can be challenging for some learners, yet the contribution of complexity to individual differences in language learning remains poorly understood. This poor understanding appears due to both a lack of consensus among researchers regarding what complexity is, and to on-line language research often employing…

Prior research has established an executive function advantage among bilinguals as compared to monolingual peers. These non-linguistic cognitive advantages are largely assumed to result from the experience of managing two linguistic systems. However, the possibility remains that the relationship between bilingualism and executive function is…