Search Results for author:
Found 22 papers, 15 papers with code
On Zero-Shot Counterspeech Generation by LLMs
In terms of prompting, we find that our proposed strategies help in improving counter speech generation across all the models.
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some existing method and need to be cross-checked (and corrected) by annotators to improve the model performance.
Zero shot VLMs for hate meme detection: Are we there yet?
In this study, we aim to investigate the efficacy of these visual language models in handling intricate tasks such as hate meme detection.
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi
With the rise of online abuse, the NLP community has begun investigating the use of neural architectures to generate counterspeech that can "counter" the vicious tone of such abusive speech and dilute/ameliorate their rippling effect over the social network.
Probing LLMs for hate speech detection: strengths and vulnerabilities
Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models.
HateMM: A Multi-Modal Dataset for Hate Video Classification
Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world.
On the rise of fear speech in online social media
Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
Rationale-Guided Few-Shot Classification to Detect Abusive Language
We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains.
Hate Speech and Offensive Language Detection in Bengali
To overcome the existing research's limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets.
Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments
The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity.
CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech
We generate counterspeech using three datasets and observe significant improvement across different attribute scores.
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
To enable more targeted diagnostic insights of such multilingual hate speech models, we introduce a set of functionalities for the purpose of evaluation.
Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach
In this FIRE 2021 shared task - "HASOC- Abusive and Threatening language detection in Urdu" the organizers propose an abusive language detection dataset in Urdu along with threatening language detection.
Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages
Hate speech is considered to be one of the major issues currently plaguing online social media.
You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
Hate speech is regarded as one of the crucial issues plaguing the online social media.
Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection
Social media often acts as breeding grounds for different forms of offensive content.
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community.
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities.
Ranked #3 on
Hate Speech Detection
on HateXplain
Deep Learning Models for Multilingual Hate Speech Detection
Hate speech detection is a challenging problem with most of the datasets available in only one language: English.
HateMonitors: Language Agnostic Abuse Detection in Social Media
In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019.
Hateminers : Detecting Hate speech against Women
With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content.
