Smatgrisene at S em E val-2020 Task 12: Offense Detection by AI - with a Pinch of Real I (2020)

Abstract: This paper discusses how ML based classifiers can be enhanced disproportionately by adding small amounts of qualitative linguistic knowledge. As an example we present the Danish classifier Smatgrisene, our contribution to the recent OffensEval Challenge 2020. The classifier was trained on 3000 social media posts annotated for offensiveness, supplemented by rules extracted from the reference work on Danish offensive language (Rathje 2014b). Smatgrisene did surprisingly well in the competition in spite of its extremely simple design, showing an interesting trade-off between technological muscle and linguistic intelligence. Finally, we comment on the perspectives in combining qualitative and quantitative methods for NLP.

## introduction offense in the social media (some) is a nuisance, not only for the targeted victim, but for editors, consumers, and stakeholders -even damaging the reputation of the internet as a whole. could we just detect and eliminate offensive some posts with the same security as we can syntax errors in python programs or even in human texts, much would be gained. the need for automatic offense detection motivated the recent offenseval challenge 2020 (sigurbergsson et al. 2020, https://arxiv.org/pdf/2006.07235.pdf ). in this paper we present our danish classifier (called smatgrisene) and report on our participation in offenseval. after introducing offenseval we discuss the annotation principles behind the shared training materials and comment on a possible consistency problem. we suggest, as a remedy, a classifier design based on a mixture of simple surface rules and synthetic linguistic knowledge. we then present our offenseval results, and conclude with a discussion of knowledge-enhanced nlp for offense detection and beyond. ## offenseval 2020 -danish chapter the danish training material consisted of 3290 some posts each annotated with labels off ('offensive') or not @xcite . index text label #443 jeg spørger lige dumt: hvad er det for et spil? not #507 var det ham der sad i en lort? off #1327 rigtig god bedring til vores dronning ... ## danish groundwork the data that form the basis for the classification of offensive language in this study, is collected in a sociolinguistic work on swear words and abusive terms @xcite . ## smatgrisene, the classifier as the technical details are without real interest (and beside the main purpose of the paper), we keep this section at a minimum. our training algorithm hardly qualifies as such: frequency-based extraction of regular expression templates from the off and not lists, thus immediately reproducible from the code (cf. https://dsn.dk/smatgrisene ). ## conclusion we have presented the classifier smatgrisene (the name being a rarely, if ever, used lexical variant of 'smatso', one of the most abusive epithets in danish), trained to detect offensive language in some posts. the training algorithm combined simple surface rules (derived from the offenseval training data) and deep linguistic knowledge (distilled from a comprehensive academic study of danish offensive language, @xcite ). despite its extreme formal simplicity, smatgrisene did unexpectedly well in the offenseval challenge 2020 and came out in the top third of participants. a real achievement, however, would be to utilize the rich metadata provided in @xcite in a future classifier for offense detection. @xcite , like others in the same tradition, analyses the perception of potentially offensive language as a function of the interlocutors' age, social standing and societal background, and of the purpose and intentions of the interchange. we surmise that such synthetic knowledge could sharpen the focus of classifiers monitoring the communication channels in chat rooms and social media. offensive communication is a complex social game. at the end of the day there is no such thing as an intrinsically offensive word.