The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency. Nevertheless, for automated proficiency classification systems, different approaches for different languages are proposed. Our paper evaluates and extends the results of an approach to Automatic Essay Scoring proposed as a part of the REPROLANG 2020 challenge. We provide a comparison between our results and the ones from the published paper and we include a new corpus for the English language for further experiments. Our results are lower than the expected ones when using the same approach and the system does not scale well with the added English corpus.

Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).