XDoc: Unified Pre-training for Cross-Format Document Understanding

6 Oct 2022  ·  Jingye Chen, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei ·

The surge of pre-training has witnessed the rapid development of document understanding recently. Pre-training and fine-tuning framework has been effectively used to tackle texts in various formats, including plain texts, document texts, and web texts. Despite achieving promising performance, existing pre-trained models usually target one specific document format at one time, making it difficult to combine knowledge from multiple document formats. To address this, we propose XDoc, a unified pre-trained model which deals with different document formats in a single model. For parameter efficiency, we share backbone parameters for different formats such as the word embedding layer and the Transformer layers. Meanwhile, we introduce adaptive layers with lightweight parameters to enhance the distinction across different formats. Experimental results have demonstrated that with only 36.7% parameters, XDoc achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models, which is cost effective for real-world deployment. The code and pre-trained models will be publicly available at \url{https://aka.ms/xdoc}.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Semantic entity labeling FUNSD XDoc1M F1 89.4 # 7