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When learning a new skill, you take advantage of your preexisting skills andknowledge. For instance, if you are a skilled violinist, you will likely havean easier time learning to play cello. Similarly, when learning a new languageyou take advantage of the languages you already speak. For instance, if yournative language is Norwegian and you decide to learn Dutch, the lexical overlapbetween these two languages will likely benefit your rate of languageacquisition. This thesis deals with the intersection of learning multiple tasksand learning multiple languages in the context of Natural Language Processing(NLP), which can be defined as the study of computational processing of humanlanguage. Although these two types of learning may seem different on thesurface, we will see that they share many similarities.  The traditional approach in NLP is to consider a single task for a singlelanguage at a time. However, recent advances allow for broadening thisapproach, by considering data for multiple tasks and languages simultaneously.This is an important approach to explore further as the key to improving thereliability of NLP, especially for low-resource languages, is to take advantageof all relevant data whenever possible. In doing so, the hope is that in thelong term, low-resource languages can benefit from the advances made in NLPwhich are currently to a large extent reserved for high-resource languages.This, in turn, may then have positive consequences for, e.g., languagepreservation, as speakers of minority languages will have a lower degree ofpressure to using high-resource languages. In the short term, answering thespecific research questions posed should be of use to NLP researchers workingtowards the same goal.

One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis