Language Understanding Model
Language understanding models aim to enable computers to comprehend and interpret human language, focusing on tasks like natural language inference and question answering. Current research emphasizes improving model robustness, particularly addressing biases in training data and handling out-of-distribution inputs, often leveraging transformer-based architectures like BERT and its variants. These advancements are crucial for applications ranging from chatbot development and media bias detection to improving the efficiency and accuracy of healthcare systems and other domains relying on natural language processing.
Papers
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs
Kyle Richardson, Ronen Tamari, Oren Sultan, Reut Tsarfaty, Dafna Shahaf, Ashish Sabharwal