A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model
Guoxin Wang, Yijuan Lu, Lei Cui, Tengchao Lv, Dinei A. F. Florencio, Cha Zhang · 2022
Positional encoding plays a key role in Transformer-based architecture, which is to indicate and embed token sequential order information.Understanding documents with unreliable reading order information is a real challenge for document Transformer models.This paper proposes a simple and effective positional encoding method, learnable sinusoidal positional encoding (LSPE), by building a learnable sinusoidal positional encoding feed-forward network.We apply LSPE to document Transformer models and pretrain them on document datasets.Then we finetune and evaluate the model performance on document understanding tasks in form, receipt, and invoice domains.Experimental results show our proposed method not only outperforms other baselines, but also demonstrates its robustness and stability on handling noisy data with incorrect order information.