@inproceedings{pengcheng-etal-2024-translate,
title = "Translate-and-Revise: Boosting Large Language Models for Constrained Translation",
author = "Pengcheng, Huang and
Yongyu, Mu and
Yuzhang, Wu and
Bei, Li and
Chunyang, Xiao and
Tong, Xiao and
Zhu, Jingbo",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.82/",
pages = "1059--1074",
language = "eng",
abstract = "{\textquotedblleft}Imposing constraints on machine translation systems presents a challenging issue because thesesystems are not trained to make use of constraints in generating adequate, fluent translations. Inthis paper, we leverage the capabilities of large language models (LLMs) for constrained trans-lation, given that LLMs can easily adapt to this task by taking translation instructions and con-straints as prompts. However, LLMs cannot always guarantee the adequacy of translation, and,in some cases, ignore the given constraints. This is in part because LLMs might be overly confi-dent in their predictions, overriding the influence of the constraints. To overcome this overidingbehaviour, we propose to add a revision process that encourages LLMs to correct the outputs byprompting them about the constraints that have not yet been met. We evaluate our approach onfour constrained translation tasks, encompassing both lexical and structural constraints in mul-tiple constraint domains. Experiments show 15{\%} improvement in constraint-based translationaccuracy over standard LLMs and the approach also significantly outperforms neural machinetranslation (NMT) state-of-the-art methods.IntroductionConstrained translation seeks to generate translations that adhere to pre-specified constraints. Toachieve this, conventional approaches impose constraints on machine translation systems and force themto follow the constraints during inference (Hokamp and Liu, 2017; Hasler et al., 2018; Dinu et al., 2019;Bergmanis and Pinnis, 2021b; Wang et al., 2022b; Ailem et al., 2022). More recently, large languagemodels (LLMs) have been shown to be strong translation systems (Hendy et al., 2023; Moslem et al.,2023). They provide a general way to involve various instructions, demonstrations, and constraints intothe translation process (Mu et al., 2023; Bogoychev and Chen, 2023), enabling us to perform constrainedtranslation using off-the-shelf, well-trained LLMs.{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pengcheng-etal-2024-translate">
<titleInfo>
<title>Translate-and-Revise: Boosting Large Language Models for Constrained Translation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Huang</namePart>
<namePart type="family">Pengcheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mu</namePart>
<namePart type="family">Yongyu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wu</namePart>
<namePart type="family">Yuzhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Li</namePart>
<namePart type="family">Bei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiao</namePart>
<namePart type="family">Chunyang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiao</namePart>
<namePart type="family">Tong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jingbo</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiye</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“Imposing constraints on machine translation systems presents a challenging issue because thesesystems are not trained to make use of constraints in generating adequate, fluent translations. Inthis paper, we leverage the capabilities of large language models (LLMs) for constrained trans-lation, given that LLMs can easily adapt to this task by taking translation instructions and con-straints as prompts. However, LLMs cannot always guarantee the adequacy of translation, and,in some cases, ignore the given constraints. This is in part because LLMs might be overly confi-dent in their predictions, overriding the influence of the constraints. To overcome this overidingbehaviour, we propose to add a revision process that encourages LLMs to correct the outputs byprompting them about the constraints that have not yet been met. We evaluate our approach onfour constrained translation tasks, encompassing both lexical and structural constraints in mul-tiple constraint domains. Experiments show 15% improvement in constraint-based translationaccuracy over standard LLMs and the approach also significantly outperforms neural machinetranslation (NMT) state-of-the-art methods.IntroductionConstrained translation seeks to generate translations that adhere to pre-specified constraints. Toachieve this, conventional approaches impose constraints on machine translation systems and force themto follow the constraints during inference (Hokamp and Liu, 2017; Hasler et al., 2018; Dinu et al., 2019;Bergmanis and Pinnis, 2021b; Wang et al., 2022b; Ailem et al., 2022). More recently, large languagemodels (LLMs) have been shown to be strong translation systems (Hendy et al., 2023; Moslem et al.,2023). They provide a general way to involve various instructions, demonstrations, and constraints intothe translation process (Mu et al., 2023; Bogoychev and Chen, 2023), enabling us to perform constrainedtranslation using off-the-shelf, well-trained LLMs.”</abstract>
<identifier type="citekey">pengcheng-etal-2024-translate</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.82/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>1059</start>
<end>1074</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Translate-and-Revise: Boosting Large Language Models for Constrained Translation
%A Pengcheng, Huang
%A Yongyu, Mu
%A Yuzhang, Wu
%A Bei, Li
%A Chunyang, Xiao
%A Tong, Xiao
%A Zhu, Jingbo
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F pengcheng-etal-2024-translate
%X “Imposing constraints on machine translation systems presents a challenging issue because thesesystems are not trained to make use of constraints in generating adequate, fluent translations. Inthis paper, we leverage the capabilities of large language models (LLMs) for constrained trans-lation, given that LLMs can easily adapt to this task by taking translation instructions and con-straints as prompts. However, LLMs cannot always guarantee the adequacy of translation, and,in some cases, ignore the given constraints. This is in part because LLMs might be overly confi-dent in their predictions, overriding the influence of the constraints. To overcome this overidingbehaviour, we propose to add a revision process that encourages LLMs to correct the outputs byprompting them about the constraints that have not yet been met. We evaluate our approach onfour constrained translation tasks, encompassing both lexical and structural constraints in mul-tiple constraint domains. Experiments show 15% improvement in constraint-based translationaccuracy over standard LLMs and the approach also significantly outperforms neural machinetranslation (NMT) state-of-the-art methods.IntroductionConstrained translation seeks to generate translations that adhere to pre-specified constraints. Toachieve this, conventional approaches impose constraints on machine translation systems and force themto follow the constraints during inference (Hokamp and Liu, 2017; Hasler et al., 2018; Dinu et al., 2019;Bergmanis and Pinnis, 2021b; Wang et al., 2022b; Ailem et al., 2022). More recently, large languagemodels (LLMs) have been shown to be strong translation systems (Hendy et al., 2023; Moslem et al.,2023). They provide a general way to involve various instructions, demonstrations, and constraints intothe translation process (Mu et al., 2023; Bogoychev and Chen, 2023), enabling us to perform constrainedtranslation using off-the-shelf, well-trained LLMs.”
%U https://aclanthology.org/2024.ccl-1.82/
%P 1059-1074
Markdown (Informal)
[Translate-and-Revise: Boosting Large Language Models for Constrained Translation](https://aclanthology.org/2024.ccl-1.82/) (Pengcheng et al., CCL 2024)
ACL