SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

Rosenthal, Sara Brin; Atanasova, Pepa; Karadzhov, Georgi E.; Zampieri, Marcos; Nakov, Preslav

doi:10.48550/arxiv.2004.14454

Cited by 20 publications

(27 citation statements)

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“…There have been different types of abusive content addressed in recent studies including hate speech [26], aggression [22,23], and cyberbullying [42]. A few annotation taxonomies, such as the one proposed by OLID [56] and replicated in other studies [43], try to take advantage of the similarities between these sub-tasks allowing us to consider multiple types of abusive language at once.…”

Section: Related Workmentioning

confidence: 99%

Multilingual Offensive Language Identification with Cross-lingual Embeddings

Ranasinghe

Zampieri

2020

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

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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.

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Section: Related Workmentioning

confidence: 99%

Multilingual Offensive Language Identification with Cross-lingual Embeddings

Ranasinghe

Zampieri

2020

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

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“…In terms of languages, the majority of studies on this topic deal with English (Malmasi and Zampieri, 2017;Yao et al, 2019;Ridenhour et al, 2020;Rosenthal et al, 2020) due to the the wide availability of language resources such as corpora and pre-trained models. In recent years, several studies have been published on identifying offensive content in other languages such as Arabic (Mubarak et al, 2020), Dutch (Tulkens et al, 2016), French (Chiril et al, 2019), Greek (Pitenis et al, 2020), Italian (Poletto et al, 2017), Portuguese (Fortuna et al, 2019), and Turkish (Çöltekin, 2020).…”

Section: Related Workmentioning

confidence: 99%

MUDES: Multilingual Detection of Offensive Spans

Ranasinghe¹,

Zampieri²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES' components is presented in this paper.

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“…Identifying Toxicity -Most work on identifying toxic language looked at a individual social media posts or comments without taking context into account (Davidson et al, 2017;Xu et al, 2012;Zampieri et al, 2019;Rosenthal et al, 2020;Kumar et al, 2018;Garibo i Orts, 2019;Ousidhoum et al, 2019;Breitfeller et al, 2019;Hada et al, 2021;Barikeri et al, 2021)…”

Section: Related Workmentioning

confidence: 99%

Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

Baheti¹,

Sap²,

Ritter³

et al. 2021

Preprint

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Dialogue models trained on human conversations inadvertently learn to generate offensive responses. Moreover, models can insult anyone by agreeing with an offensive context. To understand the dynamics of contextually offensive language, we study the stance of dialogue model responses in offensive Reddit conversations. Specifically, we crowd-annotate TOXI-CHAT, a new dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance. Our analysis reveals that 42% of user responses agree with toxic comments; 3× their agreement with safe comments (13%). Pre-trained transformer-based classifiers fine-tuned on our dataset achieve 0.71 F 1 for offensive labels and 0.53 Macro-F 1 for stance labels. Finally, we analyze some existing controllable text generation (CTG) methods to mitigate the contextual offensive behavior of dialogue models. Compared to the baseline, our best CTG model obtains a 19% reduction in agreement with offensive context and 29% fewer offensive responses. This highlights the need for future work to characterize and analyze more forms of inappropriate behavior in dialogue models to help make them safer. 1

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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

Cited by 20 publications

References 0 publications

Multilingual Offensive Language Identification with Cross-lingual Embeddings

Multilingual Offensive Language Identification with Cross-lingual Embeddings

MUDES: Multilingual Detection of Offensive Spans

Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

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