Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification

• URL: http://arxiv.org/abs/2205.07960v1
• Date: Mon, 16 May 2022 19:53:16 GMT
• Title: Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
• Authors: Badr AlKhamissi, Mona Diab
• Abstract summary: We tackle the Arabic Fine-Grained Hate Speech Detection shared task. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask.
• Score: 20.632017481940075
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask -- reflecting a 3.4% relative improvement compared to previous work.

Related papers

• Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks [61.3055230762097]
  We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning.
  arXiv  Detail & Related papers  (2023-09-14T03:13:18Z)
• Automatic Translation of Hate Speech to Non-hate Speech in Social Media Texts [0.0]
  We present a novel task of translating hate speech into non-hate speech text while preserving its meaning. We provide a dataset and several baselines as a starting point for further research. The aim of this study is to contribute to the development of more effective methods for reducing the spread of hate speech in online communities.
  arXiv  Detail & Related papers  (2023-06-02T04:03:14Z)
• DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction [50.51901599433536]
  DisfluencyFixer is a tool that performs speech-to-speech disfluency correction in English and Hindi. Our proposed system removes disfluencies from input speech and returns fluent speech as output.
  arXiv  Detail & Related papers  (2023-05-26T14:13:38Z)
• AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify [2.9220076568786326]
  We present our submission to the Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated Arabic Twitter dataset. For offensive Tweets, sub-task B focuses on detecting whether the tweet is hate speech or not. For hate speech Tweets, sub-task C focuses on detecting the fine-grained type of hate speech among six different classes.
  arXiv  Detail & Related papers  (2022-07-18T12:33:51Z)
• 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)
• Unified Speech-Text Pre-training for Speech Translation and Recognition [113.31415771943162]
  We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four self-supervised and supervised subtasks for cross modality learning. It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset.
  arXiv  Detail & Related papers  (2022-04-11T20:59:51Z)
• 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)
• 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)
• Leveraging Multilingual Transformers for Hate Speech Detection [11.306581296760864]
  We leverage state of the art Transformer language models to identify hate speech in a multilingual setting. With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages.
  arXiv  Detail & Related papers  (2021-01-08T20:23:50Z)
• Bridging the Modality Gap for Speech-to-Text Translation [57.47099674461832]
  End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously. We propose a Speech-to-Text Adaptation for Speech Translation model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text.
  arXiv  Detail & Related papers  (2020-10-28T12:33:04Z)

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.