MUDES: Multilingual Detection of Offensive Spans

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2020

Self Cite

100

Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g. hate speech, cyberbulling, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this paper, we take advantage of English data available by applying cross-lingual contextual word embeddings and transfer learning to make predictions in languages with less resources. We project predictions on comparable data in Bengali, Hindi, and Spanish and we report results of 0.8415 F1 macro for Bengali, 0.8568 F1 macro for Hindi, and 0.7513 F1 macro for Spanish. Finally, we show that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages, confirming the robustness of cross-lingual contextual embeddings and transfer learning for this task.

Section: Methodsmentioning

confidence: 99%

Multilingual Offensive Language Identification with Cross-lingual Embeddings

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2020

Self Cite

100

“…Transformer models have been used successfully for various NLP tasks [13] such as text classification [18,37], NER [39,47], context similarity [17], language identification [19], and so on. Most of the tasks were focused on English language due to the fact that most of the pre-trained transformer models were trained on English data.…”

Section: Methodsmentioning

confidence: 99%

Multilingual Offensive Language Identification for Low-resource Languages

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

2021

Self Cite

Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We project predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi, Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in TRAC-2 shared task [23], 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in OffensEval 2020 [58], 0.8568 F1 macro for Hindi in HASOC 2019 shared task [27], and 0.7513 F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) [7], showing that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages. Additionally, we report competitive performance on Arabic and Turkish using the training and development sets of OffensEval 2020 shared task. The results for all languages confirm the robustness of cross-lingual contextual embeddings and transfer learning for this task.

“…This multilingual scenario creates the need for developing more technology for local languages. This includes offensive language identification systems, which are often modeled as a supervised classification problem relying on large amounts of annotated data [14]. In this paper, we investigate strategies such as zero-shot learning and multilingual learning to circumvent data scarcity in six languages from the two most widely spoken language families in India, namely, Bengali, Hindi, and Urdu, i.e., three Indo-Aryan languages, and Kannada, Malaylam, and Tamil-the three Dravidian languages.…”

Section: Motivationmentioning

confidence: 99%

An Evaluation of Multilingual Offensive Language Identification Methods for the Languages of India

2021

Information

Self Cite

The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been published in the last few years, with promising results. The majority of these studies, however, deal with high-resource languages such as English due to the availability of datasets in these languages. Recent work has addressed offensive language identification from a low-resource perspective, exploring data augmentation strategies and trying to take advantage of existing multilingual pretrained models to cope with data scarcity in low-resource scenarios. In this work, we revisit the problem of low-resource offensive language identification by evaluating the performance of multilingual transformers in offensive language identification for languages spoken in India. We investigate languages from different families such as Indo-Aryan (e.g., Bengali, Hindi, and Urdu) and Dravidian (e.g., Tamil, Malayalam, and Kannada), creating important new technology for these languages. The results show that multilingual offensive language identification models perform better than monolingual models and that cross-lingual transformers show strong zero-shot and few-shot performance across languages.