4 citations · 16 across the 14 of their papers we have counts for
15 papers
SEEK: Semantic Evidence Extraction via Adaptive ChunKing for Multilingual Fact-Checking
Gaurav Kumar, Babu Kumar, Ayush Garg +2
Multilingual fact verification requires evidence that is both relevant and sufficiently complete for reliable factuality prediction. However, existing systems often rely on search…
Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR
Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +3
Large language models often exhibit a substantial gap between their performance in English and in lower-resourced languages on equivalent knowledge queries---a cross-lingual consis…
Neither Here Nor There: Cross-Lingual Representation Dynamics of Code-Mixed Text in Multilingual Encoders
Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +1
Multilingual encoder-based language models are widely used for code-mixed analysis, yet their internal representations of code-mixed inputs -- and their relationship to the constit…
Mind the Links: Cross-Layer Attention for Link Prediction in Multiplex Networks
Devesh Sharma, Aditya Kishore, Ayush Garg +3
Multiplex graphs capture diverse relations among shared nodes. Most predictors either collapse layers or treat them independently. This loses crucial inter-layer dependencies and s…
MultiCheck: Strengthening Web Trust with Unified Multimodal Fact Verification
Aditya Kishore, Gaurav Kumar, Jasabanta Patro
Misinformation on the web increasingly appears in multimodal forms, combining text, images, and OCR-rendered content in ways that amplify harm to public trust and vulnerable commun…
On VLMs for Diverse Tasks in Multimodal Meme Classification
Deepesh Gavit, Debajyoti Mazumder, Samiran Das +1
In this paper, we present a comprehensive and systematic analysis of vision-language models (VLMs) for disparate meme classification tasks. We introduced a novel approach that gene…
Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability
Gaurav Kumar, Ayush Garg, Debajyoti Mazumder +3
Automated fact-checking has been a challenging task for the research community. Prior work has explored various strategies, such as end-to-end training, retrieval-augmented generat…
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro
In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. Particularly, we tried three approaches: (i) native sample mi…
Improving code-mixed hate detection by native sample mixing: A case study for Hindi-English code-mixed scenario
Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro
Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the h…
IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
Tanuj Singh Shekhawat, Manoj Kumar, Udaybhan Rathore +2
This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated f…
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…
What Propels Celebrity Follower Counts? Language Use or Social Connectivity
Jasabanta Patro, Rameshwar Bhaskaran, Animesh Mukherjee
Follower count is a factor that quantifies the popularity of celebrities. It is a reflection of their power, prestige and overall social reach. In this paper we investigate whether…
Characterizing the spread of exaggerated news content over social media
Jasabanta Patro, Sabyasachee Baruah, Vivek Gupta +3
In this paper, we consider a dataset comprising press releases about health research from different universities in the UK along with a corresponding set of news articles. First, w…
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Jasabanta Patro, Bidisha Samanta, Saurabh Singh +4
In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlati…
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
Jasabanta Patro, Bidisha Samanta, Saurabh Singh +3
Code-mixing or code-switching are the effortless phenomena of natural switching between two or more languages in a single conversation. Use of a foreign word in a language; however…