Natural Language
Natural language processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. Current research heavily utilizes large language models (LLMs), such as BERT and others, to tackle diverse tasks including text-to-SQL translation, semantic analysis of images, and even controlling robots via natural language commands. The field's impact spans various sectors, from improving search engines and e-commerce platforms to advancing healthcare diagnostics and facilitating more efficient scientific research through automated literature analysis and data extraction.
Papers
A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors
Md Abrar Jahin, Subrata Talapatra
Translating Natural Language Queries to SQL Using the T5 Model
Albert Wong, Lien Pham, Young Lee, Shek Chan, Razel Sadaya, Youry Khmelevsky, Mathias Clement, Florence Wing Yau Cheng, Joe Mahony, Michael Ferri
Describing Differences in Image Sets with Natural Language
Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E. Gonzalez, Serena Yeung-Levy
Text Intimacy Analysis using Ensembles of Multilingual Transformers
Tanmay Chavan, Ved Patwardhan
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models
Alessandro Suglia, Ioannis Konstas, Oliver Lemon
Text2Loc: 3D Point Cloud Localization from Natural Language
Yan Xia, Letian Shi, Zifeng Ding, João F. Henriques, Daniel Cremers
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
Shaohua Wu, Xudong Zhao, Shenling Wang, Jiangang Luo, Lingjun Li, Xi Chen, Bing Zhao, Wei Wang, Tong Yu, Rongguo Zhang, Jiahua Zhang, Chao Wang