Grammatical Error Correction refers to the task of automatically correcting errors in written text, typically with respect to texts written by learners of a second language. The work in this thesis implements and evaluates two methods to Grammatical Error Correction for Swedish. In addition, the proposed methods are compared to an existing, rule-based system. Previous research on GEC for the Swedish language is limited and has not yet utilized the potential of neural networks. The first method implemented in this work is based on a neural machine translation approach, training a Transformer model to translate erroneous text into a corrected version. A parallel dataset containing artificially generated errors is created to train the model. The second method utilizes a Swedish version of the pre-trained language model BERT to estimate the likelihood of potential corrections in an erroneous text. Employing the SweLL gold corpus consisting of essays written by learners of Swedish, the proposed methods are evaluated using GLEU and through a manual evaluation based on the types of errors and their corresponding corrections found in the essays. The results show that the two methods correct approximately the same amount of errors, while differing in terms of which error types that are best handled. Specifically, the translation approach has a wider coverage of error types and is superior for syntactical and punctuation errors. In contrast, the language model approach yields consistently higher recall and outperforms the translation approach with regards to lexical and morphological errors. To improve the results, future work could investigate the effect of increased model size and amount of training data, as well as the potential in combining the two methods.

In an increasingly interconnected world where English has become the lingua franca of business, culture, entertainment, and academia, learners of English as a second language (L2) have been steadily growing. This has contributed to an increasing demand for automatic spoken language assessment systems for formal settings and practice situations in Computer-Assisted Language Learning. One common misunderstanding about automated assessment is the assumption that machines should replicate the human process of assessment. Instead, computers are programmed to identify, extract, and quantify features in learners' productions, which are subsequently combined and weighted in a multidimensional space to predict a proficiency level or grade. In this regard, transferring human assessment knowledge and skills into an automatic system is a challenging task since this operation should take into account the complexity and the specificities of the proficiency construct. This PhD thesis presents research conducted on methods and techniques for the automatic assessment and feedback of L2 spoken English, mainly focusing on the application of deep learning approaches. In addition to overall proficiency grades, the main forms of feedback explored in this thesis are feedback on grammatical accuracy and assessment related to particular aspects of proficiency (e.g., grammar, pronunciation, rhythm, fluency, etc.). The first study explores the use of written data and the impact of features extracted through grammatical error detection on proficiency assessment, while the second illustrates a pipeline which starts from disfluency detection and removal, passes through grammatical error correction, and ends with proficiency assessment. Grammar, as well as rhythm, pronunciation, and lexical and semantic aspects, is also considered in the third study, which investigates whether it is possible to use systems targeting specific facets of proficiency analytically when only holistic scores are available. Finally, in the last two studies, we investigate the use of self-supervised learning speech representations for both holistic and analytic proficiency assessment. While aiming at enhancing the performance of state-of-the-art automatic systems, the present work pays particular attention to the validity and interpretability of assessment both holistically and analytically and intends to pave the way to a more profound and insightful knowledge and understanding of automatic systems for speaking assessment and feedback.