NLP Community
The Natural Language Processing (NLP) community focuses on enabling computers to understand, interpret, and generate human language, driving advancements in various applications. Current research emphasizes multilingual capabilities, particularly for low-resource languages, improving model reliability and addressing biases in large language models (LLMs) and other architectures like transformers. This work is crucial for advancing fields like healthcare (e.g., dementia research), legal analysis, and education, while also raising important ethical considerations regarding data usage and model transparency.
Papers
Previously on the Stories: Recap Snippet Identification for Story Reading
Jiangnan Li, Qiujing Wang, Liyan Xu, Wenjie Pang, Mo Yu, Zheng Lin, Weiping Wang, Jie Zhou
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi
Mithun Das, Saurabh Kumar Pandey, Shivansh Sethi, Punyajoy Saha, Animesh Mukherjee
SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras
Nithya R, Malavika S, Jordan F, Arjun Gangwar, Metilda N J, S Umesh, Rithik Sarab, Akhilesh Kumar Dubey, Govind Divakaran, Samudra Vijaya K, Suryakanth V Gangashetty
Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance
Pritam Kadasi, Mayank Singh