42 citations · 93 across the 31 of their papers we have counts for
19 papers · 1 filter
CrowdCounter: A benchmark type-specific multi-target counterspeech dataset
Punyajoy Saha, Abhilash Datta, Abhik Jana +1
Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. However, writing effective counterspeech is challe…
On Zero-Shot Counterspeech Generation by LLMs
Punyajoy Saha, Aalok Agrawal, Abhik Jana +2
With the emergence of numerous Large Language Models (LLM), the usage of such models in various Natural Language Processing (NLP) applications is increasing extensively. Counterspe…
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha +2
Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impa…
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi
Mithun Das, Saurabh Kumar Pandey, Shivansh Sethi +2
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 abusi…
Exploring the Limits of Zero Shot Vision Language Models for Hate Meme Detection: The Vulnerabilities and their Interpretations
Naquee Rizwan, Paramananda Bhaskar, Mithun Das +3
There is a rapid increase in the use of multimedia content in current social media platforms. One of the highly popular forms of such multimedia content are memes. While memes have…
Probing LLMs for hate speech detection: strengths and vulnerabilities
Sarthak Roy, Ashish Harshavardhan, Animesh Mukherjee +1
Recently efforts have been made by social media platforms as well as researchers to detect hateful or toxic language using large language models. However, none of these works aim t…
A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos
Anand Kumar Rai, Siddharth D Jaiswal, Animesh Mukherjee
Automatic speech recognition (ASR) systems are designed to transcribe spoken language into written text and find utility in a variety of applications including voice assistants and…
Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection
Mithun Das, Saurabh Kumar Pandey, Animesh Mukherjee
Hate speech is a severe issue that affects many online platforms. So far, several studies have been performed to develop robust hate speech detection systems. Large language models…
HateProof: Are Hateful Meme Detection Systems really Robust?
Piush Aggarwal, Pranit Chawla, Mithun Das +4
Exploiting social media to spread hate has tremendously increased over the years. Lately, multi-modal hateful content such as memes has drawn relatively more traction than uni-moda…
Rationale-Guided Few-Shot Classification to Detect Abusive Language
Punyajoy Saha, Divyanshu Sheth, Kushal Kedia +2
Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, mode…
Hate Speech and Offensive Language Detection in Bengali
Mithun Das, Somnath Banerjee, Punyajoy Saha +1
Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender,…
Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
Sayantan Adak, Altaf Ahmad, Aditya Basu +1
A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so appar…
CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech
Punyajoy Saha, Kanishk Singh, Adarsh Kumar +2
Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online…
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
Souvic Chakraborty, Parag Dutta, Sumegh Roychowdhury +1
The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, the prol…
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models
Mithun Das, Punyajoy Saha, Binny Mathew +1
Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging chal…
Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages
Mithun Das, Somnath Banerjee, Animesh Mukherjee
Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem o…
A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification
Arijit Nag, Bidisha Samanta, Animesh Mukherjee +2
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challengi…
Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection
Debjoy Saha, Naman Paharia, Debajit Chakraborty +2
Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performan…
Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
Srijan Bansal, Vishal Garimella, Ayush Suhane +2
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to im…