@inproceedings{fischer-etal-2022-machine,
title = "Machine Translation of 16{T}h Century Letters from {L}atin to {G}erman",
author = "Fischer, Lukas and
Scheurer, Patricia and
Schwitter, Raphael and
Volk, Martin",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lt4hala-1.7",
pages = "43--50",
abstract = "This paper outlines our work in collecting training data for and developing a Latin{--}German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin{--}German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.",
}
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<abstract>This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin–German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.</abstract>
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%0 Conference Proceedings
%T Machine Translation of 16Th Century Letters from Latin to German
%A Fischer, Lukas
%A Scheurer, Patricia
%A Schwitter, Raphael
%A Volk, Martin
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F fischer-etal-2022-machine
%X This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin–German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.
%U https://aclanthology.org/2022.lt4hala-1.7
%P 43-50
Markdown (Informal)
[Machine Translation of 16Th Century Letters from Latin to German](https://aclanthology.org/2022.lt4hala-1.7) (Fischer et al., LT4HALA 2022)
ACL