We propose value retrieval with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of layout and semantics of a form. To further boost model performance, we propose a simple document language modeling (simpleDLM) strategy to improve document understanding on large-scale model pre-training. Experimental results show that our method outperforms our baselines significantly and the simpleDLM further improves our performance on value retrieval by around 17\% F1 score compared with the state-of-the-art pre-training method. Code will be made publicly available.
翻译:我们建议以任意查询方式检索类似表格的文件,以减少人的处理表格。与以往只处理一套固定的实地项目的方法不同,我们的方法根据对表格的布局和语义的理解,预测了任意查询的目标值。为了进一步提高模型性能,我们提议了一个简单的文件语言建模战略(simpleDLM),以提高对大规模示范培训前文件的理解。实验结果表明,我们的方法大大超过我们的基线,而简单的DLM进一步使我们的数值检索业绩比最先进的培训前方法提高了约17 ⁇ F1分。守则将公布于众。