Language Understanding
Language understanding research aims to enable computers to comprehend and process human language as effectively as humans do, focusing on tasks like natural language understanding (NLU) and generation (NLG). Current research emphasizes improving model robustness to noise, ambiguity, and biases, often employing transformer-based architectures, grammar induction techniques, and methods like retrieval-augmented generation and mixture-of-experts to enhance performance on diverse tasks. These advancements have significant implications for various applications, including improved chatbots, more effective machine translation, and enhanced accessibility for individuals with communication challenges.
Papers
Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation
Shaojun Xu, Xusheng Luo, Yutong Huang, Letian Leng, Ruixuan Liu, Changliu Liu
MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU
Yan Li, So-Eon Kim, Seong-Bae Park, Soyeon Caren Han