Pre-training Universal Language Representation
This work addresses the challenge of handling multiple layers of linguistic objects in a unified way for natural language processing applications.
The paper tackles the problem of learning universal language representations that embed different linguistic units and text lengths into a uniform vector space, achieving the highest accuracy on analogy tasks across language levels and significantly improving performance on GLUE benchmark and a question answering dataset.
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models. Then we empirically verify that well designed pre-training scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way. Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset.