推进手写文档问答：一种面向HW-SQuAD的最先进基于识别的模型
Advancing Question Answering on Handwritten Documents: A State-of-the-Art Recognition-Based Model for HW-SQuAD
AI总结：
针对手写文档问答任务，提出融合基于transformer的文档检索与模型层集成的新型识别方法，在HW-SQuAD、BenthamQA数据集上刷新SOTA，提升检索精度，且将开源代码与模型。
摘要：
手写文档问答是一项具有挑战性的任务，拥有众多实际应用场景。本文提出了一种新颖的基于识别的方法，在HW-SQuAD和BenthamQA数据集上超越了此前的最先进水平。我们的模型融合了基于transformer的文档检索和模型层面的集成方法，在HW-SQuAD和BenthamQA数据集上分别取得了82.02%和69%的精确匹配（Exact Match）分数，较此前最优的基于识别的方法分别提升了10.89%和3%。我们还优化了文档检索组件，将top-5检索准确率从90%提升至95.30%。研究结果证明了所提方法在推进手写文档问答方面的重要性。代码与训练好的模型将公开发布，以推动自然语言这一关键领域的未来研究。
英文摘要：
Question-answering handwritten documents is a challenging task with numerous real-world applications. This paper proposes a novel recognition-based approach that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA datasets. Our model incorporates transformer-based document retrieval and ensemble methods at the model level, achieving an Exact Match score of 82.02% and 69% in HW-SQuAD and BenthamQA datasets, respectively, surpassing the previous best recognition-based approach by 10.89% and 3%. We also enhance the document retrieval component, boosting the top-5 retrieval accuracy from 90% to 95.30%. Our results demonstrate the significance of our proposed approach in advancing question answering on handwritten documents. The code and trained models will be publicly available to facilitate future research in this critical area of natural language.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
