  Due to the complex layouts of documents, it is challenging to extract
information for documents. Most previous studies develop multimodal pre-trained
models in a self-supervised way. In this paper, we focus on the embedding
learning of word blocks containing text and layout information, and propose
UTel, a language model with Unified TExt and Layout pre-training. Specifically,
we propose two pre-training tasks: Surrounding Word Prediction (SWP) for the
layout learning, and Contrastive learning of Word Embeddings (CWE) for
identifying different word blocks. Moreover, we replace the commonly used 1D
position embedding with a 1D clipped relative position embedding. In this way,
the joint training of Masked Layout-Language Modeling (MLLM) and two newly
proposed tasks enables the interaction between semantic and spatial features in
a unified way. Additionally, the proposed UTel can process arbitrary-length
sequences by removing the 1D position embedding, while maintaining competitive
performance. Extensive experimental results show UTel learns better joint
representations and achieves superior performance than previous methods on
various downstream tasks, though requiring no image modality. Code is available
at \url{https://github.com/taosong2019/UTel}.
