The Document-based Visual Question Answering competition addresses the
automatic detection of parent-child relationships between elements in
multi-page documents. The goal is to identify the document elements that answer
a specific question posed in natural language. This paper describes the
PoliTo's approach to addressing this task, in particular, our best solution
explores a text-only approach, leveraging an ad hoc sampling strategy.
Specifically, our approach leverages the Masked Language Modeling technique to
fine-tune a BERT model, focusing on sentences containing sensitive keywords
that also occur in the questions, such as references to tables or images.
Thanks to the effectiveness of this approach, we are able to achieve high
performance compared to baselines, demonstrating how our solution contributes
positively to this task.

本文描述了 PoliTo 对文档视觉问答竞赛的方法，特别是我们利用文本方法和特定的采样策略，通过细调 BERT 模型，关注包含敏感关键词的句子以回答自然语言问题，如引用表格或图片的问题，以实现高性能的结果。

关键词驱动的句子选择增强基于 BERT 的视觉问答

Enhancing BERT-Based Visual Question Answering through Keyword-Driven  Sentence Selection

The problem of document structure reconstruction refers to converting digital
or scanned documents into corresponding semantic structures. Most existing
works mainly focus on splitting the boundary of each element in a single
document page, neglecting the reconstruction of semantic structure in
multi-page documents. This paper introduces hierarchical reconstruction of
document structures as a novel task suitable for NLP and CV fields. To better
evaluate the system performance on the new task, we built a large-scale dataset
named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million
semantic units. Every document in HRDoc has line-level annotations including
categories and relations obtained from rule-based extractors and human
annotators. Moreover, we proposed an encoder-decoder-based hierarchical
document structure parsing system (DSPS) to tackle this problem. By adopting a
multi-modal bidirectional encoder and a structure-aware GRU decoder with
soft-mask operation, the DSPS model surpass the baseline method by a large
margin. All scripts and datasets will be made publicly available at
this https URL.

本文提出了一种新的任务：层次化重建多页文档的语义结构，并介绍了基于编码 - 解码结构的层次性文档结构分析系统 (DSPS) 来解决该问题。通过采用多模式双向编码器和结构感知 GRU 解码器，以及软屏蔽操作，DSPS 模型的表现优于基线方法。此外，作者还构建了一个大规模数据集 HRDoc，并公开发布。

HRDoc: 文档结构的分层重构的数据集和基准方法

HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures

Document Visual Question Answering (DocVQA) refers to the task of answering
questions from document images. Existing work on DocVQA only considers
single-page documents. However, in real scenarios documents are mostly composed
of multiple pages that should be processed altogether. In this work we extend
DocVQA to the multi-page scenario. For that, we first create a new dataset,
MP-DocVQA, where questions are posed over multi-page documents instead of
single pages. Second, we propose a new hierarchical method, Hi-VT5, based on
the T5 architecture, that overcomes the limitations of current methods to
process long multi-page documents. The proposed method is based on a
hierarchical transformer architecture where the encoder summarizes the most
relevant information of every page and then, the decoder takes this summarized
information to generate the final answer. Through extensive experimentation, we
demonstrate that our method is able, in a single stage, to answer the questions
and provide the page that contains the relevant information to find the answer,
which can be used as a kind of explainability measure.

本研究扩展了针对单页文档的 DocVQA 任务到多页文档的场景，提出了一个基于 T5 架构的新的分层方法 Hi-VT5，用于处理长的多页文档，并且通过实验证明本方法可以在单阶段完成回答问题和提供包含答案相关信息的页码，该信息可以作为可解释性量度。