IREL at S em E val-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks (2023)

Abstract: This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.

## introduction natural language expressions, such as sentences and phrases, can often have multiple possible interpretations depending on the context in which they are used. this ambiguity arises due to language's inherent complexity and flexibility, which can lead to different interpretations of the same expression by different individuals. additionally, subjective tasks can lead to disagreements between annotators with different perspectives or interpretations of the same text. the semeval-2023 task 11 learning with disagreements (le-wi-di) @xcite focuses entirely on such subjective tasks, where training with aggregated labels makes much less sense. in this task, we worked with three (textual) datasets with different characteristics in terms of languages (english and arabic), tasks (misogyny, hate speech, offensiveness detection) and annotations' methodology (experts, specific demographic groups, amt-crowd). we leverage this additional 1 https://github.com/ankita-maity/lewidi information in order to get more accurate estimates of each annotator's annotation. all the datasets provide a multiplicity of labels for each instance. the focus is on developing methods able to capture agreements/disagreements rather than focusing on developing the best model. since a "truth" cannot be assumed, "soft" evaluation is the primary form of evaluating performances, i.e. an evaluation that considers how well the model's probabilities reflect the level of agreement among annotators. ## related work this is the 2nd edition of the le-wi-di task; the previous version was held in semeval 2021 @xcite . a survey paper by @xcite was also released, which identified several nlp and cv tasks for which the gold-standard idealisation has been shown not to hold. it used them to analyse the extensive literature on learning from data possibly containing disagreements. @xcite who introduced the hs-brexit dataset, trained different classifiers for each annotator, and then took an ensemble of classifiers. in the case of multiclass problems (for example, classifying different kinds of hate speech instead of simply distinguishing between hate speech vs non-hate speech), there have been efforts to frame it as which class is harder to classify instead of which text belongs to which class @xcite . for tasks without annotated labels to calculate soft loss, augmentation techniques like mixup, as shown by @xcite , could be used to distribute the probability mass over more than one class. ## data the three datasets we worked with all deal with twitter data -hs-brexit @xcite , armis @xcite and multidomain agreement dataset @xcite . while hs-brexit and multidomain agreement deal with english tweets, armis deals with arabic tweets. details of these datasets are given in the "hs-brexit" dataset is a new dataset of tweets on abusive language on brexit and annotated for hate speech (hs), aggressiveness and offensiveness by six annotators belonging to two distinct groups: a target group of three muslim immigrants in the uk, and a control group of three other individuals. the "armis" dataset is a dataset of arabic tweets annotated for misogyny and sexism detection by annotators with different demographic characteristics ("moderate female", "liberal female", and "conservative male"). the "multidomain agreement" dataset comprises around 10k english tweets from three domains (blm, election, covid-19). each tweet is annotated for offensiveness by five annotators via amt. particular focus was put on annotating preselected tweets that could lead to disagreement. ## methodology since the datasets we used have both textual information and external information, we needed to combine this information to derive meaningful insights. this combination can be done in various ways -from simply concatenating the information to using the attention-based summation of the information. the main models we used were bertweet (nguyen et al., 2020) for hs-brexit and md-agreement, and arabert (antoun et al., 2020) for armis. lm-based text embeddings were common across all datasets, but other embeddings we used varied based on the datasets. for hs-brexit, we used embeddings for aggressiveness and offensiveness information. for md-agreement, we first concatenated the tweet's domain information to the tweet's text. also, since there were over 800 annotators for md-agreement, we needed additional embeddings to capture this. for this, we used onehot vectors and let the model learn information about the annotators based on their annotations. in our experiments, we concatenate the embeddings and then use attention-based mixing as a combining module. we use auxiliary losses to improve model performance. figure 1 shows a high-level overview of this structure. for our experiments, we use adam optimizer with a learning rate of 1e-6 and cycliclr scheduler with triangular2 mode. we train the model for 30 epochs with a batch size of 16. initially, we use hard labels (for the submission) but later also experiment with soft labels and apply softmax over the logits produced by the classifier. uma et al. (2020) found that soft-loss training systematically outperforms gold training when the objective is to achieve a model whose output mimics most closely the distribution of labels produced by the annotators. we compare the effects of using soft loss training with respect to hard loss training on the given datasets, as explained in the results section. for the final experiment, we improve our mixers and add more complexity to the model. we experiment with multi-head attention-based mixing for this. the final embedding is obtained after three layers of multi-head attention-based mixing followed by feed-forward layers. since, for armis, no additional information was present, this dataset was excluded from this experiment. ## conclusion when there is no clear answer that all annotators can agree with, it is crucial to consider the factor of their disagreement over just getting a single label for a data point. our results highlight the benefits of using soft loss over hard loss for such controversial cases. we also find that using better ways to