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Joint Modelling of Emotion and Abusive Language Detection
[article]
2020
arXiv
pre-print
Preserved Fulltext
In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. ...
of the users and how this might affect their language. ...
For instance, we expect that abuse detection may also benefit from joint learning with complex semantic tasks, such as figurative language processing and inference. ...
arXiv:2005.14028v1
fatcat:nnltfnth4fb57npktge3gwu5xe
Joint Modelling of Emotion and Abusive Language Detection
2020
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
unpublished
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In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. ...
of the users and how this might affect their language. ...
For instance, we expect that abuse detection may also benefit from joint learning with complex semantic tasks, such as figurative language processing and inference. ...
doi:10.18653/v1/2020.acl-main.394
fatcat:pqdpepmt35abtfqatkgugph7hu
A Multitask Learning Framework for Abuse Detection and Emotion Classification
2022
Algorithms
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pretrained language model. ...
Then, we used two different decoders for emotion classification and abuse detection, respectively. ...
used to detect abusive language. ...
doi:10.3390/a15040116
fatcat:huu43b2hxfewxklwcbngwuan3e
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
2018
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection ...
, and personality recognition. ...
For small datasets, sentiment analysis, emotion classification, sarcasm detection, abusive language classification, stress detection, insult classification and personality recognition are included. ...
doi:10.18653/v1/w18-6243
dblp:conf/wassa/XuMWPF18
fatcat:j7nwip3fnvh6hmypnukl37ut5q
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
[article]
2021
arXiv
pre-print
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We conduct ablation studies and case studies to empirically examine the strengths and characteristics of our AngryBERT model and show that the secondary tasks are able to improve hate speech detection. ...
Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. ...
To the best of our knowledge, AngryBERT is the first model that uses a pre-trained and fine-tuned language model in a MTL framework for hate speech detection. Contributions. ...
arXiv:2103.11800v1
fatcat:zys2mgloa5gubeoyf36unqvqqu
Towards multidomain and multilingual abusive language detection: a survey
2021
Personal and Ubiquitous Computing
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Having a robust model to detect abusive instances automatically is a prominent challenge. ...
AbstractAbusive language is an important issue in online communication across different platforms and languages. ...
In the early phases of cross-domain abusive language detection, specific models which adopt joint-learning [115] and multi-task [145] architectures achieved the best performance. ...
doi:10.1007/s00779-021-01609-1
fatcat:ufiyagb6grel7ojjkyhb2vjtrm
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language
[article]
2022
arXiv
pre-print
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The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task to decide if a text contains HOF. ...
We base our experiments on existing data sets for each of these concepts (sentiment, emotion, target of HOF) and evaluate our models as a participant (as team IMS-SINAI) in the HASOC FIRE 2021 English ...
Acknowledgement This work has been partially supported by a grant from European Regional Development Fund (FEDER), LIVING-LANG project [RTI2018-094653-B-C21], and Ministry of Science, Innovation and Universities ...
arXiv:2109.10255v4
fatcat:3dq5tgelovhgpc6w25a4bv6bpq
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
2021
Cognitive Computation
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EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. ...
approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating ...
Finally, the most recent work by [109] came up with a joint model of emotion and abusive language detection in a MTL setting. ...
doi:10.1007/s12559-021-09862-5
pmid:34221180
pmcid:PMC8236572
fatcat:742czn3qvnep5gt6hkaoccz75m
Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages
[article]
2022
arXiv
pre-print
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We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. ...
Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. ...
We looked into several datasets for Indic languages for abusive speech detection and attempted to gather all of them. ...
arXiv:2204.12543v1
fatcat:e63ezns76vdvlgcyhab7zg3tae
Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention
[article]
2021
arXiv
pre-print
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Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. ...
We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. ...
detection and categorization of abusive language. ...
arXiv:2105.11119v1
fatcat:2u2ltkykzvh37gyj5tlk7nthkm
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
[article]
2021
arXiv
pre-print
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However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and ...
In order to address this, we investigate the efficacy of multi-annotator models. ...
We also thank TACL action editor, Dirk Hovy, and the anonymous reviewers for their constructive feedback. ...
arXiv:2110.05719v1
fatcat:axrszawyd5acfkmtadwrwtpgmy
Emotion Based Hate Speech Detection using Multimodal Learning
[article]
2022
arXiv
pre-print
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Our results demonstrate that incorporating emotional attributes leads to significant improvement over text-based models in detecting hateful multimedia content. ...
Consequently, there have been substantial research efforts towards automated detection of such content using Natural Language Processing (NLP). ...
Hence, we experiment with multiple hate speech detection datasets for the text models and an acoustic dataset consisting of a varying range of emotional attributes for the emotion model. ...
arXiv:2202.06218v1
fatcat:5iwxp4wfonclvlt7hxaetlnf5q
C3-Sex: A Conversational Agent to Detect Online Sex Offenders
2020
Electronics
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The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. ...
The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. ...
grammatical) and in emotion (emotionally consistent). ...
doi:10.3390/electronics9111779
fatcat:u5334jqu3bfk7l7huykdymlxuy
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as 'most blacks use abusive language', or 'fear is a virtue of ...
Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone ...
Davidson, Bhattacharya, and Weber (2019) demonstrated racial bias in four different tweet data sets used for hate and abusive language detection. ...
doi:10.1609/aaai.v34i03.5654
fatcat:rmaguvhr6fc2dhqtkrkusztd6e
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
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classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as 'most blacks use abusive language', or 'fear is a virtue of ...
Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone ...
Davidson, Bhattacharya, and Weber (2019) demonstrated racial bias in four different tweet data sets used for hate and abusive language detection. ...
arXiv:2005.06618v2
fatcat:2y7i4vybsje67pwmgtylai5zky
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