# Two Demonstrations of the Machine Translation Applications to Historical Documents

arXiv: https://arxiv.org/abs/2102.01417 · HTML: https://mlsanity.com/paper/2102.01417/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation](https://mlsanity.com/paper/1808.10432/) (2018)
- [Online Learning for Effort Reduction in Interactive Neural Machine Translation](https://mlsanity.com/paper/1802.03594/) (2018)
- [Modernizing Historical Documents: a User Study](https://mlsanity.com/paper/1907.00659/) (2019)
- [NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning](https://mlsanity.com/paper/1807.03096/) (2018)

## Abstract (arXiv)

We present our demonstration of two machine translation applications to historical documents. The first task consists in generating a new version of a historical document, written in the modern version of its original language. The second application is limited to a document's orthography. It adapts the document's spelling to modern standards in order to achieve an orthography consistency and accounting for the lack of spelling conventions. We followed an interactive, adaptive framework that allows the user to introduce corrections to the system's hypothesis. The system reacts to these corrections by generating a new hypothesis that takes them into account. Once the user is satisfied with the system's hypothesis and validates it, the system adapts its model following an online learning strategy. This system is implemented following a client-server architecture. We developed a website which communicates with the neural models. All code is open-source and publicly available. The demonstration is hosted at http://demosmt.prhlt.upv.es/mthd/.