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Generating text at the right level of complexity for its target audience so it can be easily understood by its target audience has the potential to make information more accessible to a wider range of people, including non-native speakers, language learners, and people who suffer from language or cognitive impairments. For example, a native Hindi speaker learning English might prefer reading a U.S. news article in English or Hindi tailored to their vocabulary and language proficiency level. Natural Language Generation (NLG), the use of computational models to generate human-like text, has been used to empower countless applications – from automatically summarizing financial and weather reports to enabling communication between multilingual communities through automatic translation. Although NLG has met some level of success, current models ignore that there are many valid ways of conveying the same information in a text and that selecting the appropriate variation requires knowing who the text is written for and its intended purpose.

To address this, in this thesis, we present tasks, datasets, models, and algorithms that are designed to let users specify how simple or complex the generated text should be in a given language. We introduce the Complexity Controlled Machine Translation task, where the goal is to translate text from one language to another at a specific complexity level defined by the U.S. reading grade level. While standard machine translation (MT) tools generate a single output for each input, the models we design for this task produce translation at various complexity levels to suit the needs of different users. In order to build such models, we ideally require rich annotation and resources for supervised training, i.e., examples of the same input text paired with several translations in the output language, which is not available in most datasets used in MT. Hence, we have also contributed datasets that can enable the generation and evaluation of complexity-controlled translations. Furthermore, recognizing that when humans simplify a complex text in a given language, they often revise parts of the complex text according to the intended audience, we present strategies to adopt general-purpose Edit-based Non-Autoregressive models for controllable text simplification (TS). In this framing, the simplified output for a desired grade level is generated through a sequence of edit operations like deletions and insertions applied to the complex input sequence. As the model needs to learn to perform a wide range of edit operations for different target grade levels, we introduce algorithms to inject additional guidance during training and inference, which results in improved output quality while also providing users with the specific changes made to the input text. Finally, we present approaches to adapt general-purpose controllable TS models that leverage unsupervised pre-training and low-level control tokens describing the nature of TS edit operations as side constraints for grade-specific TS.

Having developed models that can enable complexity-controlled text generation, in the final part of the thesis, we introduce a reading comprehension-based human evaluation framework that is designed to assess the correctness of texts generated by these systems using multiple-choice question-answering. Furthermore, we evaluate whether the measure of correctness (via the ability of native speakers to answer the questions correctly using the simplified texts) is captured by existing automatic metrics that measure text complexity or meaning preservation.