Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning (2022)

Abstract: Annotating abusive language is expensive, logistically complex and creates a risk of psychological harm. However, most machine learning research has prioritized maximizing effectiveness (i.e., F1 or accuracy score) rather than data efficiency (i.e., minimizing the amount of data that is annotated). In this paper, we use simulated experiments over two datasets at varying percentages of abuse to demonstrate that transformers-based active learning is a promising approach to substantially raise efficiency whilst still maintaining high effectiveness, especially when abusive content is a smaller percentage of the dataset. This approach requires a fraction of labeled data to reach performance equivalent to training over the full dataset.

## introduction online abuse, such as hate and harassment, can inflict psychological harm on victims @xcite , disrupt communities @xcite and even lead to physical attacks @xcite . machine learning solutions can be used to automatically detect abusive content at scale, helping to tackle this growing problem @xcite ). an effective model is one which makes few misclassifications, minimizing the risk of harm from false positives and negatives: false negatives mean that users are not fully protected from abuse while false positives constrain free expression. most models to automatically detect abuse are trained to maximize effectiveness via "passive" supervised learning over large labeled datasets. however, although collecting large amounts of social media data is relatively cheap and easy, annotating data is expensive, logistically complicated and creates a risk of inflicting psychological harm on annotators through vicarious trauma @xcite . thus, an efficient model, which achieves a given level of performance with few labeled examples, is highly desirable for abusive content detection. our central objective is to demonstrate how to maximize efficiency and effectiveness when training abuse detection systems, and in this paper, we focus on active learning (al). al is an iterative human-in-the-loop approach that selects entries for annotation only if they are 'informative @xcite . while al has shown promise for abusive language dataset creation @xcite @xcite @xcite , there are several open questions about the most appropriate configuration and use. in particular, only one paper uses transformersbased al for abusive language detection (ein-dor et al., 2020) to our knowledge, although the benefits of al for other classification tasks is clear @xcite @xcite . pre-trained transformer models have been widely adopted for abuse detection, but while they can be fine-tuned on relatively few examples for specific tasks @xcite , they are still commonly used with large datasets (e.g. @xcite @xcite . our first subquestion asks, rq1.1: what effect do model pre-training and architecture have on efficiency and effectiveness? to answer rq1.1, we evaluate transformers-and traditional-based al in a simulated setup using two already-labeled abusive language datasets. one challenge in abusive language detection is class imbalance as, although extremely harmful, abuse comprises a small portion of online content @xcite . prior al work primarily uses datasets at their given class imbalances and thus has not disentangled how class imbalance versus linguistic features affect the design choices needed for efficient al. this is a problem given that most abusive language datasets do not reflect the imbalance actually observed in the wild. our second subquestion addresses this issue, rq1.2: what effect does class imbalance have on efficiency and effectiveness? to answer rq1.2, we artificiallyrebalance the datasets at different percentages of abuse. in addressing these questions, we find that more data is not always better and can actually be worse, showing that effectiveness and efficiency are not always in tension with one another. with extensive pre-training and greater model complexity, a transformers-based al approach achieves high performance with only a few hundred examples. crucially, we show that the value of transformers-based al (relative to random sampling) is larger for more imbalanced data (i.e., data that more closely reflects the real-world). for 5% abuse, the performance of a transformers-based al strategy over 3% of a 20k dataset can even surpass the f1 of a model passively trained over the full dataset by 5 percentage points (fig. 1 ). in §4 we describe caveats of our findings and implications for future research in abusive language detection. foot_0 2 methods ## discussion in response to our central research objective, we find strategies which are both effective and efficient, requiring far fewer examples to reach performance equivalent to passive training over the full dataset. these results suggest that passive approaches may be needlessly expensive and place annotators at unnecessary risk of harm. for rq1.1, we find that coupling pre-trained transformers with al is a successful approach which leverages the benefits of careful training data selection with the previously demonstrated strong capabilities of pre-trained language models for few-shot learning @xcite @xcite .