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Register a new userGeneralisability of Topic Models in Cross-corpora Abusive Language Detection
التمويلية لنماذج الموضوع في الكشف عن لغة الصليب والنباتية
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يتطلب محتوى الوسائط الاجتماعية المتغيرة بسرعة لنماذج الكشف عن إساءة الاستخدام قوية وممتع.ومع ذلك، فإن النماذج الخاضعة للإشراف على أحدث حدوث عرض الأداء المتدهورة عند تقييمها بشأن التعليقات المسيئة التي تختلف عن Training Corpus.نحقق في ما إذا كان أداء النماذج الخاضعة للإشراف للكشف عن إساءة استخدام سوريا يمكن تحسينه من خلال دمج معلومات إضافية من نماذج الموضوع، حيث يمكن أن يستنتج الأخير مخاليط الموضوعات الكامنة من العينات غير المرئية.على وجه الخصوص، نجمع بين المعلومات الموضعية مع التمثيلات من نموذج تم ضبطه لتصنيف التعليقات المسيئة.يكشف تحليل الأداء الخاص بنا أن نماذج الموضوعات قادرة على التقاط الموضوعات المتعلقة بالإساءة التي يمكنها نقلها عبر كوربورا، وتؤدي إلى تحسين التبرعات.
Rapidly changing social media content calls for robust and generalisable abuse detection models. However, the state-of-the-art supervised models display degraded performance when they are evaluated on abusive comments that differ from the training corpus. We investigate if the performance of supervised models for cross-corpora abuse detection can be improved by incorporating additional information from topic models, as the latter can infer the latent topic mixtures from unseen samples. In particular, we combine topical information with representations from a model tuned for classifying abusive comments. Our performance analysis reveals that topic models are able to capture abuse-related topics that can transfer across corpora, and result in improved generalisability.
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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.
From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performa
nce on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.
Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical a
nalysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a single-objective Bayesian optimization. This allows us to determine the robustness of a topic model for several evaluation metrics. We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other.
Abusive language detection has become an important tool for the cultivation of safe online platforms. We investigate the interaction of annotation quality and classifier performance. We use a new, fine-grained annotation scheme that allows us to dist
inguish between abusive language and colloquial uses of profanity that are not meant to harm. Our results show a tendency of crowd workers to overuse the abusive class, which creates an unrealistic class balance and affects classification accuracy. We also investigate different methods of distinguishing between explicit and implicit abuse and show lexicon-based approaches either over- or under-estimate the proportion of explicit abuse in data sets.
Current abusive language detection systems have demonstrated unintended bias towards sensitive features such as nationality or gender. This is a crucial issue, which may harm minorities and underrepresented groups if such systems were integrated in r
eal-world applications. In this paper, we create ad hoc tests through the CheckList tool (Ribeiro et al., 2020) to detect biases within abusive language classifiers for English. We compare the behaviour of two BERT-based models, one trained on a generic hate speech dataset and the other on a dataset for misogyny detection. Our evaluation shows that, although BERT-based classifiers achieve high accuracy levels on a variety of natural language processing tasks, they perform very poorly as regards fairness and bias, in particular on samples involving implicit stereotypes, expressions of hate towards minorities and protected attributes such as race or sexual orientation. We release both the notebooks implemented to extend the Fairness tests and the synthetic datasets usable to evaluate systems bias independently of CheckList.
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