Nowadays, social media platforms use classification models to cope with hate speech and abusive language. The problem of these models is their vulnerability to bias. A prevalent form of bias in hate speech and abusive language datasets is annotator b
ias caused by the annotator's subjective perception and the complexity of the annotation task. In our paper, we develop a set of methods to measure annotator bias in abusive language datasets and to identify different perspectives on abusive language. We apply these methods to four different abusive language datasets. Our proposed approach supports annotation processes of such datasets and future research addressing different perspectives on the perception of abusive language.
We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we hav
e curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.
As socially unacceptable language become pervasive in social media platforms, the need for automatic content moderation become more pressing. This contribution introduces the Dutch Abusive Language Corpus (DALC v1.0), a new dataset with tweets manual
ly an- notated for abusive language. The resource ad- dress a gap in language resources for Dutch and adopts a multi-layer annotation scheme modeling the explicitness and the target of the abusive messages. Baselines experiments on all annotation layers have been conducted, achieving a macro F1 score of 0.748 for binary classification of the explicitness layer and .489 for target classification.
The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario. As human annotation involves substantial time and effort, models th
at can adapt to newly collected comments can prove to be useful. In this paper, we investigate the effectiveness of several Unsupervised Domain Adaptation (UDA) approaches for the task of cross-corpora abusive language detection. In comparison, we adapt a variant of the BERT model, trained on large-scale abusive comments, using Masked Language Model (MLM) fine-tuning. Our evaluation shows that the UDA approaches result in sub-optimal performance, while the MLM fine-tuning does better in the cross-corpora setting. Detailed analysis reveals the limitations of the UDA approaches and emphasizes the need to build efficient adaptation methods for this task.
The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance. The use of these mechanisms allows one managing the importance of the elements of a sequence in accordan
ce to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence. The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results. It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.
Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i
.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.
In this paper we discuss several challenges related to the development of a 3D game, whose goal is to raise awareness on cyberbullying while collecting linguistic annotation on offensive language. The game is meant to be used by teenagers, thus raisi
ng a number of issues that need to be tackled during development. For example, the game aesthetics should be appealing for players belonging to this age group, but at the same time all possible solutions should be implemented to meet privacy requirements. Also, the task of linguistic annotation should be possibly hidden, adopting so-called orthogonal game mechanics, without affecting the quality of collected data. While some of these challenges are being tackled in the game development, some others are discussed in this paper but still lack an ultimate solution.
