This paper introduces the Swedish Mu-ClaGED dataset, a new dataset specifically built for the task of Multi-Class Grammatical Error Detection (GED). The dataset has been produced as a part of the multilingual Computational SLA shared task initiative. In this paper we elaborate on the generation process and the design choices made to obtain Swedish MuClaGED. We also show initial baseline results for the performance on the dataset in a task of Grammatical Error Detection and Classification on the sentence level, which have been obtained through (Bi)LSTM ((Bidirectional) Long-Short Term Memory) methods.

We report on our work-in-progress to generate a synthetic error dataset for Swedish by replicating errors observed in the authentic errorannotated dataset. We analyze a small subset of authentic errors, capture regular patterns based on parts of speech, and design a set of rules to corrupt new data. We explore the approach and identify its capabilities, advantages and limitations as a way to enrich the existing collection of error-annotated data. This work focuses on word order errors, specifically those involving the placement of finite verbs in a sentence.