He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation

no code implementations • Findings (ACL) 2021 • Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan

Language Modelling

InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction

no code implementations • 24 May 2023 • Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber

Learning template based information extraction from documents is a crucial yet difficult task.

Question Answering Question Generation +1

LayerDoc: Layer-wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents

no code implementations • IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 • Puneet Mathur, Rajiv Jain, Ashutosh Mehra, Jiuxiang Gu, Franck Dernoncourt, Anandhavelu N, Quan Tran, Verena Kaynig-Fittkau, Ani Nenkova, Dinesh Manocha, Vlad I. Morariu

Experiments show that our approach outperforms competitive baselines by 10-15% on three diverse datasets of forms and mobile app screen layouts for the tasks of spatial region classification, higher-order group identification, layout hierarchy extraction, reading order detection, and word grouping.

Reading Order Detection

CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

no code implementations • EMNLP 2021 • Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan, Anandhavelu N, Rajiv Jain

We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context.