Toxic content is one of the most critical issues for social media platforms today. India alone had 518 million social media users in 2020. In order to provide a good experience to content creators and their audience, it is crucial to flag toxic comments and the users who post that. But the big challenge is identifying toxicity in low resource Indic languages because of the presence of multiple representations of the same text. Moreover, the posts/comments on social media do not adhere to a particular format, grammar or sentence structure; this makes the task of abuse detection even more challenging for multilingual social media platforms. This paper describes the system proposed by team 'Moj Masti' using the data provided by ShareChat/Moj in \emph{IIIT-D Multilingual Abusive Comment Identification} challenge. We focus on how we can leverage multilingual transformer based pre-trained and fine-tuned models to approach code-mixed/code-switched classification tasks. Our best performing system was an ensemble of XLM-RoBERTa and MuRIL which achieved a Mean F-1 score of 0.9 on the test data/leaderboard. We also observed an increase in the performance by adding transliterated data. Furthermore, using weak metadata, ensembling and some post-processing techniques boosted the performance of our system, thereby placing us 1st on the leaderboard.
UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages
Indian Institute of Technology Gandhinagar（印度理工学院加尔各答分校）；Indian Institute of Technology Goa（印度理工学院果阿分校）；Indian Institute of Technology Tirupati（印度理工学院蒂鲁普蒂）
This paper describes our participation in the DEtection of TOXicity in comments In Spanish (DETOXIS) shared task 2021 at the 3rd Workshop on Iberian Languages Evaluation Forum. The shared task is divided into two related classification tasks: (i) Task 1: toxicity detection and; (ii) Task 2: toxicity level detection. They focus on the xenophobic problem exacerbated by the spread of toxic comments posted in different online news articles related to immigration. One of the necessary efforts towards mitigating this problem is to detect toxicity in the comments. Our main objective was to implement …
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
Institute for Logic, Language and Computation, University of Amsterdam（逻辑、语言与计算研究所，阿姆斯特丹大学）；Center for Language and Cognition, University of Groningen（语言与认知中心，格罗宁根大学）
This report provides an engagement analysis of counternarratives against online toxicity. Between February 2020 and July 2021, we observed over 15 million toxic messages on social media identified by our fine-grained, multilingual detection AI. Over 1,000 dashboard users responded to toxic messages with combinations of visual memes, text, or AI-generated text, or they reported content. This leads to new, real-life insights on self-regulatory approaches for the mitigation of online hate.
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；National University of Singapore（新加坡国立大学）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Federal University of São Carlos（圣卡洛斯联邦大学）；University of Sheffield（谢菲尔德大学）
Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capable of causing harm. Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media. Previous work in automatically detecting toxic comments focus mainly in English, with very few work in languages like Brazilian Portuguese. In this paper, we propose a new large-scale dataset for Brazilian Portuguese with tweets annotated as either toxic or non-toxic…
Maya: An Instruction Finetuned Multilingual Multimodal Model
Cohere For AI Community（Cohere 人工智能社区）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Imperial College London（伦敦帝国学院）；Georgia Institute of Technology（佐治亚理工学院）；The Alan Turing Institute（艾伦·图灵研究所）；Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；University of Pennsylvania（宾夕法尼亚大学）；IIT Bombay（印度理工学院孟买分校）；TU Darmstadt（达姆施塔特工业大学）；IIT Dhanbad（印度理工学院丹巴德分校）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Cisco Meraki（思科 Meraki）；Articul8 AI（Articul8 人工智能公司）；Capital One（第一资本金融公司）
Pretrained large language models have become indispensable for solving various natural language processing (NLP) tasks. However, safely deploying them in real world applications is challenging because they generate toxic content. To address this challenge, we propose two novel pretraining data augmentation strategies that significantly reduce model toxicity without compromising its utility. Our two strategies are: (1) MEDA: adds raw toxicity score as meta-data to the pretraining samples, and (2) INST: adds instructions to those samples indicating their toxicity. Our results indicate that our b…
Toxicity in Twitch Chats: An LLM-Based Analysis Across Gaming Communities
Institute for Information Processing（信息处理研究所）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Department of Computer Science（计算机科学系）；Technische Hochschule Mannheim（曼海姆技术学院）；Institute for Machine Learning（机器学习研究所）；Johannes Kepler University（约翰·凯普勒大学）；SDU Metaverse Lab（SDU元宇宙实验室）；University of Southern Denmark（南部丹麦大学）
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the web is a highly multilingual, cross-cultural community that develops its own lingo over time. As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles. In this paper, we present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach …
Transformer-based language models are able to generate fluent text and be efficiently adapted across various natural language generation tasks. However, language models that are pretrained on large unlabeled web text corpora have been shown to suffer from degenerating toxic content and social bias behaviors, consequently hindering their safe deployment. Various detoxification methods were proposed to mitigate the language model's toxicity; however, these methods struggled to detoxify language models when conditioned on prompts that contain specific social identities related to gender, race, or…
While there has been significant progress towards developing NLU resources for Indic languages, syntactic evaluation has been relatively less explored. Unlike English, Indic languages have rich morphosyntax, grammatical genders, free linear word-order, and highly inflectional morphology. In this paper, we introduce Vyākarana: a benchmark of Colorless Green sentences in Indic languages for syntactic evaluation of multilingual language models. The benchmark comprises four syntax-related tasks: PoS Tagging, Syntax Tree-depth Prediction, Grammatical Case Marking, and Subject-Verb Agreement. We use…
IIT Madras（印度理工学院马德拉斯分校）；Columbia University（哥伦比亚大学）；National Institute of Information and Communications Technology（信息通信研究机构）；Microsoft（微软）；University of Edinburgh（爱丁堡大学）；AI4Bharat
Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. In this paper, we present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence mo…
Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus, revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Problem on Moj, an Indian video-sharing social networking service, powered by ShareChat. The problem dealt with detecting abusive comments, in 13 regional Indic languages such as Hin…
