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To understand and infer meaning in language, neural models have to learncomplicated nuances. Discovering distinctive linguistic phenomena from data isnot an easy task. For instance, lexical ambiguity is a fundamental feature oflanguage which is challenging to learn. Even more prominently, inferring themeaning of rare and unseen lexical units is difficult with neural networks.Meaning is often determined from context. With context, languages allow meaningto be conveyed even when the specific words used are not known by the reader.To model this learning process, a system has to learn from a few instances incontext and be able to generalize well to unseen cases. The learning process ishindered when training data is scarce for a task. Even with sufficient data,learning patterns for the long tail of the lexical distribution is challenging.In this thesis, we focus on understanding certain potentials of contexts inneural models and design augmentation models to benefit from them. We focus onmachine translation as an important instance of the more general languageunderstanding problem. To translate from a source language to a targetlanguage, a neural model has to understand the meaning of constituents in theprovided context and generate constituents with the same meanings in the targetlanguage. This task accentuates the value of capturing nuances of language andthe necessity of generalization from few observations. The main problem westudy in this thesis is what neural machine translation models learn from dataand how we can devise more focused contexts to enhance this learning. Lookingmore in-depth into the role of context and the impact of data on learningmodels is essential to advance the NLP field. Moreover, it helps highlight thevulnerabilities of current neural networks and provides insights into designingmore robust models.

Understanding and Enhancing the Use of Context for Machine Translation