A Neighborhood Framework for Resource-Lean Content Flagging

A Neighborhood Framework for Resource-Lean Content Flagging Sheikh Muhammad Sarwar2,5,∗ and Dimitrina Zlatkova1 and Momchil Hardalov1,6 and Yoan Dinkov1 and Isabelle Augenstein1,3 and Preslav Nakov1,4 1Checkstep, UK, 2University of Massachusetts, Amherst, 4Qatar Computing Research Institute, HBKU, Qatar3University of Copenhagen, Denmark, 5Amazon.com, US 6Sofia University ‘‘St. Kliment Ohridski’’, Bulgaria smsarwar@amazon.com, {didi,momchil,yoan.dinkov,isabelle,preslav.nakov}@checkstep.com Abstract We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its compo- nents. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encod- ing schemes and we show their effectiveness using both qualitative and quantitative analy- sis. Our evaluation results on eight languages from two different datasets for abusive lan- guage detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset. 1 Introduction Online content moderation is an increasingly important problem—small-scale websites and large-scale corporations alike strive to remove harmful content from their platforms (Vidgen et al., 2019; Pavlopoulos et al., 2017; Wulczyn et al., 2017). This is partly in anticipation of pro- posed legislation, such as the Digital Service Act (European Commission, 2020) in the EU and the Online Harms Bill (UK Government, 2020) in the UK. Moreover, the lack of content moderation can have significant impact on businesses (e.g., Parler was denied server space), on governments ∗Work done prior to joining Amazon (e.g., the U.S. Capitol Riots), and on individuals (e.g., because hate speech is linked to self-harm [J¨urgens et al., 2019]). A key challenge when developing content mod- eration systems is the lack of resources for many languages (other than English). With this in mind, here we aim to create a content flagging model for a target language with limited annotated data by transferring knowledge from another dataset in a different language, for which a large amount of training data is available. Various approaches have been proposed in the literature to address the lack of enough training data in the target language. A popular approach is to fine-tune large-scale pre-trained multilingual language models such as XLM (Conneau and Lample, 2019), XLM-R (Conneau et al., 2020), or mBERT (Devlin et al., 2019) on the target data- set (Glavaˇs et al., 2020; Stappen et al., 2020). In order to incorporate knowledge from the source dataset, a sequential adaptation technique can be used that first fine-tunes a multilingual language model (LM) on the source dataset, and then on the target dataset (Garg et al., 2020). There are also existing approaches for mixing the source and the target datasets (Shnarch …

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