@inproceedings{nieminen-etal-2025-incorporating,
title = "Incorporating Target Fuzzy Matches into Neural Fuzzy Repair",
author = {Nieminen, Tommi and
Tiedemann, J{\"o}rg and
Virpioja, Sami},
editor = "Johansson, Richard and
Stymne, Sara",
booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2025.nodalida-1.44/",
pages = "408--418",
ISBN = "978-9908-53-109-0",
abstract = "Neural fuzzy repair (NFR) is a simple implementation of retrieval-augmented translation (RAT), based on data augmentation. In NFR, a translation database is searched for translation examples where the source sentence is similar to the sentence being translated, and the target side of the example is concatenated with the source sentences. We experiment with introducing retrieval that is based on target similarity to NFR during training. The results of our experiments confirm that including target similarity matches during training supplements source similarity matches and leads to better translations at translation time."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nieminen-etal-2025-incorporating">
<titleInfo>
<title>Incorporating Target Fuzzy Matches into Neural Fuzzy Repair</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tommi</namePart>
<namePart type="family">Nieminen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jörg</namePart>
<namePart type="family">Tiedemann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sami</namePart>
<namePart type="family">Virpioja</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Richard</namePart>
<namePart type="family">Johansson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Stymne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>University of Tartu Library</publisher>
<place>
<placeTerm type="text">Tallinn, Estonia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">978-9908-53-109-0</identifier>
</relatedItem>
<abstract>Neural fuzzy repair (NFR) is a simple implementation of retrieval-augmented translation (RAT), based on data augmentation. In NFR, a translation database is searched for translation examples where the source sentence is similar to the sentence being translated, and the target side of the example is concatenated with the source sentences. We experiment with introducing retrieval that is based on target similarity to NFR during training. The results of our experiments confirm that including target similarity matches during training supplements source similarity matches and leads to better translations at translation time.</abstract>
<identifier type="citekey">nieminen-etal-2025-incorporating</identifier>
<location>
<url>https://aclanthology.org/2025.nodalida-1.44/</url>
</location>
<part>
<date>2025-03</date>
<extent unit="page">
<start>408</start>
<end>418</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Incorporating Target Fuzzy Matches into Neural Fuzzy Repair
%A Nieminen, Tommi
%A Tiedemann, Jörg
%A Virpioja, Sami
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F nieminen-etal-2025-incorporating
%X Neural fuzzy repair (NFR) is a simple implementation of retrieval-augmented translation (RAT), based on data augmentation. In NFR, a translation database is searched for translation examples where the source sentence is similar to the sentence being translated, and the target side of the example is concatenated with the source sentences. We experiment with introducing retrieval that is based on target similarity to NFR during training. The results of our experiments confirm that including target similarity matches during training supplements source similarity matches and leads to better translations at translation time.
%U https://aclanthology.org/2025.nodalida-1.44/
%P 408-418
Markdown (Informal)
[Incorporating Target Fuzzy Matches into Neural Fuzzy Repair](https://aclanthology.org/2025.nodalida-1.44/) (Nieminen et al., NoDaLiDa 2025)
ACL
- Tommi Nieminen, Jörg Tiedemann, and Sami Virpioja. 2025. Incorporating Target Fuzzy Matches into Neural Fuzzy Repair. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 408–418, Tallinn, Estonia. University of Tartu Library.