Do you want to publish a course? Click here

HateBERT: Retraining BERT for Abusive Language Detection in English

HATERBERT: إعادة تدريب بيرت للكشف عن اللغة المسيئة باللغة الإنجليزية

649   0   0   0.0 ( 0 )
 Publication date 2021
and research's language is English
 Created by Shamra Editor




Ask ChatGPT about the research

We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.



References used
https://aclanthology.org/
rate research

Read More

Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social medi a, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.
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.
The advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into another. These have increased the content acces sible in different languages, which results in easily performing translated plagiarism, which are referred to as cross-language plagiarism''. Recognition of plagiarism among texts in different languages is more challenging than identifying plagiarism within a corpus written in the same language. This paper proposes a new technique for enhancing English-Arabic cross-language plagiarism detection at the sentence level. This technique is based on semantic and syntactic feature extraction using word order, word embedding and word alignment with multilingual encoders. Those features, and their combination with different machine learning (ML) algorithms, are then used in order to aid the task of classifying sentences as either plagiarized or non-plagiarized. The proposed approach has been deployed and assessed using datasets presented at SemEval-2017. Analysis of experimental data demonstrates that utilizing extracted features and their combinations with various ML classifiers achieves promising results.
Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis task.
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.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا