49 citations · 158 across the 19 of their papers we have counts for
19 papers
PROSLEX: A Novel Dataset for Expert-Annotated Legal Statute Prediction for Indian Judiciary
Subinay Adhikary, Upal Bhattacharya, Vivek Kumar Singh +6
Legal Statute Prediction (LSP) involves automatically identifying relevant legal statutes given factual descriptions in legal documents, typically framed as a multi-label classific…
SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages
Subham Kumar, Prakrithi Shivaprakash, Abhishek Manoharan +7
Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare context r…
REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra +2
Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of…
Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media
Thi Huyen Nguyen, Koustav Rudra, Wolfgang Nejdl
Advances in social media data dissemination enable the provision of real-time information during a crisis. The information comes from different classes, such as infrastructure dama…
GRACE: Graph Neural Networks for Locus-of-Care Prediction under Extreme Class Imbalance
Subham Kumar, Lekhansh Shukla, Animesh Mukherjee +2
Determining the appropriate locus of care for addiction patients is one of the most critical clinical decisions that affects patient treatment outcomes and effective use of resourc…
Data Augmentation for Sample Efficient and Robust Document Ranking
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh +2
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Jurek Leonhardt, Henrik Müller, Koustav Rudra +3
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resourc…
Understanding Lexical Biases when Identifying Gang-related Social Media Communications
Dhiraj Murthy, Constantine Caramanis, Koustav Rudra
Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing. However,…
A Review of the Role of Causality in Developing Trustworthy AI Systems
Niloy Ganguly, Dren Fazlija, Maryam Badar +9
State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not gene…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Rajdeep Mukherjee, Uppada Vishnu, Hari Chandana Peruri +4
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account o…
Efficient Neural Ranking using Forward Indexes
Jurek Leonhardt, Koustav Rudra, Megha Khosla +2
Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized…
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Zijian Zhang, Koustav Rudra, Avishek Anand
Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the inf…
Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
Paheli Bhattacharya, Soham Poddar, Koustav Rudra +2
Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this…
Extractive Explanations for Interpretable Text Ranking
Jurek Leonhardt, Koustav Rudra, Avishek Anand
Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of…
An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
Koustav Rudra, Zeon Trevor Fernando, Avishek Anand
Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passa…
Explain and Predict, and then Predict Again
Zijian Zhang, Koustav Rudra, Avishek Anand
A desirable property of learning systems is to be both effective and interpretable. Towards this goal, recent models have been proposed that first generate an extractive explanatio…
Stance Detection in Web and Social Media: A Comparative Study
Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh +2
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies fo…
Summarizing Situational and Topical Information During Crises
Koustav Rudra, Siddhartha Banerjee, Niloy Ganguly +3
The use of microblogging platforms such as Twitter during crises has become widespread. More importantly, information disseminated by affected people contains useful information li…