13 citations · 35 across the 14 of their papers we have counts for
14 papers
LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCo
Mandira Sawkar, Samay U. Shetty, Deepak Pandita +2
The Learning With Disagreements (LeWiDi) 2025 shared task aims to model annotator disagreement through soft label distribution prediction and perspectivist evaluation, which focuse…
ProRefine: Inference-Time Prompt Refinement with Textual Feedback
Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Parag Shah +3
Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications…
Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala
Shanilka Haturusinghe, Tharindu Cyril Weerasooriya, Marcos Zampieri +2
Accurate detection of offensive language is essential for a number of applications related to social media safety. There is a sharp contrast in performance in this task between low…
Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech
Jonathan Pofcher, Christopher M. Homan, Randall Sell +1
This paper makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users…
ARTICLE: Annotator Reliability Through In-Context Learning
Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya +3
Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsica…
Rater Cohesion and Quality from a Vicarious Perspective
Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta +5
Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Man…
DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Lora Aroyo, Alex S. Taylor, Mark Diaz +5
Machine learning approaches often require training and evaluation datasets with a clear separation between positive and negative examples. This risks simplifying and even obscuring…
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
Tharindu Cyril Weerasooriya, Sujan Dutta, Tharindu Ranasinghe +3
Offensive speech detection is a key component of content moderation. However, what is offensive can be highly subjective. This paper investigates how machine and human moderators d…
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi
Saurabh Gaikwad, Tharindu Ranasinghe, Marcos Zampieri +1
The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable excep…
Improving Label Quality by Jointly Modeling Items and Annotators
Tharindu Cyril Weerasooriya, Alexander G. Ororbia, Christopher M. Homan
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering…
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction
Abhinandan Desai, Kai North, Marcos Zampieri +1
This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). The task organizers provided participants with an augmented version…
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger +2
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-spec…
Neighborhood-based Pooling for Population-level Label Distribution Learning
Tharindu Cyril Weerasooriya, Tong Liu, Christopher M. Homan
Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution lea…
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
Michael Leventhal, Allahsera Tapo, Sarah Luger +2
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university student…