Target-Side Augmentation for Document-Level Machine Translation

Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) m...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-06
Main Authors: Bao, Guangsheng, Teng, Zhiyang, Zhang, Yue
Format: Article
Language:English
Subjects:
Data augmentation
Documents
Learning
Machine translation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Bao, Guangsheng
Teng, Zhiyang
Zhang, Yue
description Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2811360087</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2811360087</sourcerecordid><originalsourceid>FETCH-proquest_journals_28113600873</originalsourceid><addsrcrecordid>eNqNiskKwjAUAIMgWLT_EPAcyNIlNxEXPOjJ3kuorzUlJprF73fBD_A0MDMTlHEhGJEF5zOUhzBSSnlV87IUGVo1yg8QyVlfAK_TcAMbVdTO4t55vHVd-hhyhCcYfFLdVVvAjVc2mO-2QNNemQD5j3O03O-azYHcvXskCLEdXfL2nVouGRMVpbIW_10v7ys4oA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2811360087</pqid></control><display><type>article</type><title>Target-Side Augmentation for Document-Level Machine Translation</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>Bao, Guangsheng ; Teng, Zhiyang ; Zhang, Yue</creator><creatorcontrib>Bao, Guangsheng ; Teng, Zhiyang ; Zhang, Yue</creatorcontrib><description>Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Data augmentation ; Documents ; Learning ; Machine translation</subject><ispartof>arXiv.org, 2023-06</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2811360087?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>776,780,25731,36989,44566</link.rule.ids></links><search><creatorcontrib>Bao, Guangsheng</creatorcontrib><creatorcontrib>Teng, Zhiyang</creatorcontrib><creatorcontrib>Zhang, Yue</creatorcontrib><title>Target-Side Augmentation for Document-Level Machine Translation</title><title>arXiv.org</title><description>Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.</description><subject>Data augmentation</subject><subject>Documents</subject><subject>Learning</subject><subject>Machine translation</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNiskKwjAUAIMgWLT_EPAcyNIlNxEXPOjJ3kuorzUlJprF73fBD_A0MDMTlHEhGJEF5zOUhzBSSnlV87IUGVo1yg8QyVlfAK_TcAMbVdTO4t55vHVd-hhyhCcYfFLdVVvAjVc2mO-2QNNemQD5j3O03O-azYHcvXskCLEdXfL2nVouGRMVpbIW_10v7ys4oA</recordid><startdate>20230604</startdate><enddate>20230604</enddate><creator>Bao, Guangsheng</creator><creator>Teng, Zhiyang</creator><creator>Zhang, Yue</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20230604</creationdate><title>Target-Side Augmentation for Document-Level Machine Translation</title><author>Bao, Guangsheng ; Teng, Zhiyang ; Zhang, Yue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_28113600873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Data augmentation</topic><topic>Documents</topic><topic>Learning</topic><topic>Machine translation</topic><toplevel>online_resources</toplevel><creatorcontrib>Bao, Guangsheng</creatorcontrib><creatorcontrib>Teng, Zhiyang</creatorcontrib><creatorcontrib>Zhang, Yue</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bao, Guangsheng</au><au>Teng, Zhiyang</au><au>Zhang, Yue</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Target-Side Augmentation for Document-Level Machine Translation</atitle><jtitle>arXiv.org</jtitle><date>2023-06-04</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2023-06
issn 2331-8422
language eng
recordid cdi_proquest_journals_2811360087
source Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects Data augmentation
Documents
Learning
Machine translation
title Target-Side Augmentation for Document-Level Machine Translation
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T13%3A59%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Target-Side%20Augmentation%20for%20Document-Level%20Machine%20Translation&rft.jtitle=arXiv.org&rft.au=Bao,%20Guangsheng&rft.date=2023-06-04&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2811360087%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_28113600873%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2811360087&rft_id=info:pmid/&rfr_iscdi=true