For the analysis of political discourse a reliable identification of group references, i.e., linguistic components that refer to individuals or groups of people, is useful. However, the task of automatically recognizing group references has not yet gained much attention within NLP. To address this gap, we introduce GRIT (Group Reference for Italian), a large-scale, multi-domain manually annotated dataset for group reference recognition in Italian. GRIT represents a new resource for automatic and generalizable recognition of group references. With this dataset, we aim to establish group reference recognition as a valid classification task, which extends the domain of Named Entity Recognition by expanding its focus to literal and figurative mentions of social groups. We verify the potential of achieving automated group reference recognition for Italian through an experiment employing a fine-tuned BERT model. Our experimental results substantiate the validity of the task, implying a huge potential for applying automated systems to multiple fields of analysis, such as political text or social media analysis.

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting The study of bias, fairness and social impact in Natural Language Processing (NLP) lacks resources in languages other than English. Our objective is to support the evaluation of bias in language models in a multilingual setting. We use stereotypes across nine types of biases to build a corpus containing contrasting sentence pairs, one sentence that presents a stereotype concerning an underadvantaged group and another minimally changed sentence, concerning a matching advantaged group. We build on the French CrowS-Pairs corpus and guidelines to provide translations of the existing material into seven additional languages. In total, we produce 11,139 new sentence pairs that cover stereotypes dealing with nine types of biases in seven cultural contexts. We use the final resource for the evaluation of relevant monolingual and multilingual masked language models. We find that language models in all languages favor sentences that express stereotypes in most bias categories. The process of creating a resource that covers a wide range of language types and cultural settings highlights the difficulty of bias evaluation, in particular comparability across languages and contexts.