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
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Shoujie Tong, Heming Xia, Damai Dai, Runxin Xu, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
Zefan Cai, Xin Zheng, Tianyu Liu, Xu Wang, Haoran Meng, Jiaqi Han, Gang Yuan, Binghuai Lin, Baobao Chang, Yunbo Cao
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
Uri Shaham, Maor Ivgi, Avia Efrat, Jonathan Berant, Omer Levy
WYWEB: A NLP Evaluation Benchmark For Classical Chinese
Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong, Yin Zhang
Can Large Language Models Capture Dissenting Human Voices?
Noah Lee, Na Min An, James Thorne
Generalized Multiple Intent Conditioned Slot Filling
Harshil Shah, Arthur Wilcke, Marius Cobzarenco, Cristi Cobzarenco, Edward Challis, David Barber
Measuring and Mitigating Local Instability in Deep Neural Networks
Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan