12 citations · 14 across the 7 of their papers we have counts for
7 papers
DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding
Manan Suri, Puneet Mathur, Franck Dernoncourt +5
Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests. Past works have shown that multim…
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Ramit Sawhney, Atula Tejaswi Neerkaje, Ivan Habernal +1
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance…
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
Ramit Sawhney, Shivam Agarwal, Vivek Mittal +3
The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as c…
The Impact of Differential Privacy on Group Disparity Mitigation
Victor Petrén Bach Hansen, Atula Tejaswi Neerkaje, Ramit Sawhney +2
The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally comp…
ADIMA: Abuse Detection In Multilingual Audio
Vikram Gupta, Rini Sharon, Ramit Sawhney +1
Abusive content detection in spoken text can be addressed by performing Automatic Speech Recognition (ASR) and leveraging advancements in natural language processing. However, ASR…
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms
Gyanesh Anand, Akash Gautam, Puneet Mathur +3
Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations t…
#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement
Akash Gautam, Puneet Mathur, Rakesh Gosangi +3
In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hat…