Robust Natural Language Understanding

Robust Natural Language Understanding (NLU) aims to create AI systems that can understand human language reliably, even in challenging real-world scenarios. Current research focuses on mitigating biases in models, improving their performance on out-of-distribution data and handling noisy or ambiguous inputs, often leveraging techniques like attention mechanisms and ensemble methods within architectures such as BERT. These advancements are crucial for building more reliable and trustworthy NLP applications across diverse domains, from dialogue systems to smart home automation, ultimately bridging the gap between current NLU capabilities and human-level understanding.