Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages

• URL: http://arxiv.org/abs/2112.09301v1
• Date: Fri, 17 Dec 2021 03:28:54 GMT
• Title: Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
• Authors: Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schaefer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini and Amit Kumar Jaiswal
• Abstract summary: This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively.
• Score: 4.267837363677351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.

Related papers

• CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving [61.73180469072787]
  We focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture COSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules. COSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.
  arXiv  Detail & Related papers  (2024-06-16T16:10:51Z)
• SeamlessM4T: Massively Multilingual & Multimodal Machine Translation [90.71078166159295]
  We introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-text translation, and automatic speech recognition for up to 100 languages. We developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation.
  arXiv  Detail & Related papers  (2023-08-22T17:44:18Z)
• Adversarial Training For Low-Resource Disfluency Correction [50.51901599433536]
  We propose an adversarially-trained sequence-tagging model for Disfluency Correction (DC) We show the benefit of our proposed technique, which crucially depends on synthetically generated disfluent data, by evaluating it for DC in three Indian languages. Our technique also performs well in removing stuttering disfluencies in ASR transcripts introduced by speech impairments.
  arXiv  Detail & Related papers  (2023-06-10T08:58:53Z)
• Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi [15.466844451996051]
  The HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three subtracks for English, Hindi, and Marathi. We report the results of the HASOC 2022 Marathi subtrack which provided participants with a dataset containing data from Twitter manually annotated using the popular OLID taxonomy. The best performing algorithms were a mixture of traditional and deep learning approaches.
  arXiv  Detail & Related papers  (2022-11-18T11:17:15Z)
• Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021 [50.591267188664666]
  We present two shared tasks of abusive and threatening language detection for the Urdu language. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. For both subtasks, m-Bert based transformer model showed the best performance.
  arXiv  Detail & Related papers  (2022-07-14T07:38:13Z)
• Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss [0.0]
  Large language models are trained on a lot of data and they also make use of contextual embeddings. The data is also quite unbalanced; so we used a modified cross-entropy loss to tackle the issue. Our team (HNLP) achieved the macro F1-scores of 0.808, 0.639 in English Subtask A and English Subtask B respectively.
  arXiv  Detail & Related papers  (2022-02-05T20:31:40Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Fine-tuning of Pre-trained Transformers for Hate, Offensive, and Profane Content Detection in English and Marathi [0.0]
  This paper describes neural models developed for the Hate Speech and Offensive Content Identification in English and Indo-Aryan languages. For English subtasks, we investigate the impact of additional corpora for hate speech detection to fine-tune transformer models. For the Marathi tasks, we propose a system based on the Language-Agnostic BERT Sentence Embedding (LaBSE)
  arXiv  Detail & Related papers  (2021-10-25T07:11:02Z)
• One to rule them all: Towards Joint Indic Language Hate Speech Detection [7.296361860015606]
  We present a multilingual architecture using state-of-the-art transformer language models to jointly learn hate and offensive speech detection. On the provided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651 for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification of sub-task 1B.
  arXiv  Detail & Related papers  (2021-09-28T13:30:00Z)
• Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages [2.927129789938848]
  The HASOC track intends to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively.
  arXiv  Detail & Related papers  (2021-08-12T19:02:53Z)
• Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection [55.445023584632175]
  We build an offensive language detection system, which combines multi-task learning with BERT-based models. Our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place.
  arXiv  Detail & Related papers  (2020-04-28T11:27:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.