{"id":"https://openalex.org/W2963949210","doi":"https://doi.org/10.18653/v1/p18-2048","title":"Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation","display_name":"Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2963949210","doi":"https://doi.org/10.18653/v1/p18-2048","mag":"2963949210"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-2048","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2048","pdf_url":"https://www.aclweb.org/anthology/P18-2048.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-2048.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100431149","display_name":"Rui Wang","orcid":"https://orcid.org/0000-0001-8007-2503"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Rui Wang","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021667085","display_name":"Masao Utiyama","orcid":"https://orcid.org/0000-0003-1111-9245"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masao Utiyama","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033318800","display_name":"Eiichiro Sumita","orcid":"https://orcid.org/0000-0002-1028-4399"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Eiichiro Sumita","raw_affiliation_strings":["National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology (NICT) 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan","institution_ids":["https://openalex.org/I90023481"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100431149"],"corresponding_institution_ids":["https://openalex.org/I90023481"],"apc_list":null,"apc_paid":null,"fwci":3.7463,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.94678851,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"298","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.8725161552429199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8421295881271362},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7911279201507568},{"id":"https://openalex.org/keywords/nist","display_name":"NIST","score":0.7252297401428223},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5685542821884155},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5577692985534668},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5320338606834412},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5261042714118958},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43132734298706055},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.420652836561203},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3584195375442505}],"concepts":[{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.8725161552429199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8421295881271362},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7911279201507568},{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.7252297401428223},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5685542821884155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5577692985534668},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5320338606834412},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5261042714118958},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43132734298706055},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.420652836561203},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3584195375442505},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-2048","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2048","pdf_url":"https://www.aclweb.org/anthology/P18-2048.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-2048","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2048","pdf_url":"https://www.aclweb.org/anthology/P18-2048.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963949210.pdf","grobid_xml":"https://content.openalex.org/works/W2963949210.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W222053410","https://openalex.org/W1902237438","https://openalex.org/W2101105183","https://openalex.org/W2133564696","https://openalex.org/W2524428287","https://openalex.org/W2525778437","https://openalex.org/W2537667581","https://openalex.org/W2540404261","https://openalex.org/W2584268338","https://openalex.org/W2594229957","https://openalex.org/W2595715041","https://openalex.org/W2613904329","https://openalex.org/W2740743644","https://openalex.org/W2741838462","https://openalex.org/W2756978580","https://openalex.org/W2760656271","https://openalex.org/W2963366389","https://openalex.org/W2963403868","https://openalex.org/W2963643655","https://openalex.org/W2963932569","https://openalex.org/W2964265128","https://openalex.org/W2964308564","https://openalex.org/W3204406378","https://openalex.org/W4301368689","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W3107474891","https://openalex.org/W1544285860","https://openalex.org/W2377856297","https://openalex.org/W1978971213","https://openalex.org/W2807593602","https://openalex.org/W2507994462","https://openalex.org/W2126129064","https://openalex.org/W2553534256","https://openalex.org/W3093501075","https://openalex.org/W2398825887"],"abstract_inverted_index":{"Traditional":[0],"Neural":[1],"machine":[2],"translation":[3],"(NMT)":[4],"involves":[5],"a":[6,57,82,105],"fixed":[7],"training":[8,96,139],"procedure":[9],"where":[10],"each":[11,17,87,103],"sentence":[12,88],"is":[13,84],"sampled":[14,112],"once":[15],"during":[16,25],"epoch.":[18],"In":[19,79],"reality,":[20],"some":[21],"sentences":[22,36,45,71,109],"are":[23,110],"well-learned":[24,35],"the":[26,34,70,76,91,95,121,125,131,137,142],"initial":[27],"few":[28],"epochs;":[29],"however,":[30],"using":[31],"this":[32,80],"approach,":[33,81],"would":[37],"continue":[38],"to":[39,67,74,86,114],"be":[40],"trained":[41],"along":[42],"with":[43],"those":[44],"that":[46,130],"were":[47],"not":[48],"well":[49],"learned":[50],"for":[51],"10-30":[52],"epochs,":[53],"which":[54],"results":[55,118],"in":[56,72,102],"wastage":[58],"of":[59,98,108],"time.":[60],"Here,":[61],"we":[62],"propose":[63],"an":[64],"efficient":[65],"method":[66,133],"dynamically":[68,111],"sample":[69],"order":[73],"accelerate":[75,136],"NMT":[77,138,143],"training.":[78],"weight":[83],"assigned":[85],"based":[89,119],"on":[90,120],"measured":[92],"difference":[93],"between":[94],"costs":[97],"two":[99],"iterations.":[100],"Further,":[101],"epoch,":[104],"certain":[106],"percentage":[107],"according":[113],"their":[115],"weights.":[116],"Empirical":[117],"NIST":[122],"Chinese-to-English":[123],"and":[124,140],"WMT":[126],"English-to-German":[127],"tasks":[128],"show":[129],"proposed":[132],"can":[134],"significantly":[135],"improve":[141],"performance.":[144]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
