activity
20172024
most citedAn End-to-End Network for Emotion-Cause Pair Extraction

21 citations · 70 across the 34 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CL2024

Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs

Sanjeet Singh, Shreya Gupta, Niralee Gupta +4

The consequences of a healthcare data breach can be devastating for the patients, providers, and payers. The average financial impact of a data breach in recent months has been est…

cs.CL20241 cited

iSign: A Benchmark for Indian Sign Language Processing

Abhinav Joshi, Romit Mohanty, Mounika Kanakanti +4

Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. Though text/audio-based language processing…

cs.CL20241 cited

IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning

Abhinav Joshi, Shounak Paul, Akshat Sharma +3

Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and un…

cs.CL2024

IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings

Shubham Patel, Divyaksh Shukla, Ashutosh Modi

This paper presents our approach for the SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversations. For the Emotion Recognition in Conversations (ERC) task, w…

cs.CL2024

IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes

Shreenaga Chikoti, Shrey Mehta, Ashutosh Modi

Memes are one of the most popular types of content used in an online disinformation campaign. They are primarily effective on social media platforms since they can easily reach man…

cs.CL2024

IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts

Udvas Basak, Rajarshi Dutta, Shivam Pandey +1

This paper describes our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness. The challenge is focused on automatically detecting the degree of relatedness b…