�ݺ�ߣ

This document discusses the development of new machine learning techniques for natural language processing. It notes that deep learning approaches using neural networks have achieved state-of-the-art results for many NLP tasks by learning complex features from large amounts of text. However, these models still lack the ability to understand language with the same depth and breadth as humans. The document proposes new self-supervised learning methods that utilize vast amounts of unlabeled text to help models learn linguistic structure and commonsense knowledge. This may enable NLP systems to comprehend language with human-level understanding.Read less