# Modernizing Historical Documents: a User Study

## TL;DR

This paper presents a neural machine translation approach to modernize historical documents, aiming to reduce language barriers and improve accessibility for broader audiences, validated through automatic, human evaluations, and a user study.

## Contribution

Introduces a novel neural machine translation method leveraging modern documents to enhance modernization of historical texts.

## Key findings

- Modernization improves comprehension for broader audiences.
- The approach is effective but has room for further enhancement.
- User study confirms successful goal achievement.

## Abstract

Accessibility to historical documents is mostly limited to scholars. This is due to the language barrier inherent in human language and the linguistic properties of these documents. Given a historical document, modernization aims to generate a new version of it, written in the modern version of the document's language. Its goal is to tackle the language barrier, decreasing the comprehension difficulty and making historical documents accessible to a broader audience. In this work, we proposed a new neural machine translation approach that profits from modern documents to enrich its systems. We tested this approach with both automatic and human evaluation, and conducted a user study. Results showed that modernization is successfully reaching its goal, although it still has room for improvement.