{"id":"https://openalex.org/W2410082850","doi":"https://doi.org/10.18653/v1/w16-2209","title":"Linguistic Input Features Improve Neural Machine Translation","display_name":"Linguistic Input Features Improve Neural Machine Translation","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2410082850","doi":"https://doi.org/10.18653/v1/w16-2209","mag":"2410082850"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w16-2209","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2209","pdf_url":null,"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 First Conference on Machine Translation: Volume 1,\n          Research Papers","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/w16-2209","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005771535","display_name":"Rico Sennrich","orcid":"https://orcid.org/0000-0002-1438-4741"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rico Sennrich","raw_affiliation_strings":["School of Informatics"],"affiliations":[{"raw_affiliation_string":"School of Informatics","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110781707","display_name":"Barry Haddow","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barry Haddow","raw_affiliation_strings":["School of Informatics"],"affiliations":[{"raw_affiliation_string":"School of Informatics","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005771535"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":24.4398,"has_fulltext":false,"cited_by_count":76,"citation_normalized_percentile":{"value":0.99478388,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"83","last_page":"91"},"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":0.9858999848365784,"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/computer-science","display_name":"Computer science","score":0.8209563493728638},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.7778741121292114},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.7700730562210083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6199187636375427},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.593883216381073},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5897451639175415},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5559812784194946},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5464802980422974},{"id":"https://openalex.org/keywords/rule-based-machine-translation","display_name":"Rule-based machine translation","score":0.5010502338409424},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4703861474990845},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46337974071502686},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.45462512969970703},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4155750274658203},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32838577032089233},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.2669471502304077},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.2045624852180481}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8209563493728638},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.7778741121292114},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.7700730562210083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6199187636375427},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.593883216381073},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5897451639175415},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5559812784194946},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5464802980422974},{"id":"https://openalex.org/C53893814","wikidata":"https://www.wikidata.org/wiki/Q7378909","display_name":"Rule-based machine translation","level":2,"score":0.5010502338409424},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4703861474990845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46337974071502686},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.45462512969970703},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4155750274658203},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32838577032089233},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2669471502304077},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2045624852180481},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.18653/v1/w16-2209","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2209","pdf_url":null,"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 First Conference on Machine Translation: Volume 1,\n          Research Papers","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1606.02892","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.02892","pdf_url":"https://arxiv.org/pdf/1606.02892","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2410082850","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1606.02892.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:pure.ed.ac.uk:openaire/ce77de04-231e-4339-843c-c82e1175223d","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/ce77de04-231e-4339-843c-c82e1175223d","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sennrich, R & Haddow, B 2016, Linguistic Input Features Improve Neural Machine Translation. in Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers. Berlin, Germany, pp. 83-91, First Conference on Machine Translation, Berlin, Germany, 11/08/16. https://doi.org/10.18653/v1/W16-2209","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/ce77de04-231e-4339-843c-c82e1175223d","is_oa":true,"landing_page_url":"https://aclweb.org/anthology/papers/W/W16/W16-2209/","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sennrich, R & Haddow, B 2016, Linguistic Input Features Improve Neural Machine Translation. in Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers. Berlin, Germany, pp. 83-91, First Conference on Machine Translation, Berlin, Germany, 11/08/16. https://doi.org/10.18653/v1/W16-2209","raw_type":"contributionToPeriodical"},{"id":"doi:10.48550/arxiv.1606.02892","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1606.02892","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.18653/v1/w16-2209","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w16-2209","pdf_url":null,"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 First Conference on Machine Translation: Volume 1,\n          Research Papers","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W287510790","https://openalex.org/W1996430422","https://openalex.org/W2040711288","https://openalex.org/W2113788796","https://openalex.org/W2118167915","https://openalex.org/W2141599568","https://openalex.org/W2168579166","https://openalex.org/W2171421863","https://openalex.org/W2250342921","https://openalex.org/W2250861254","https://openalex.org/W2252272516","https://openalex.org/W2508117065","https://openalex.org/W2916548775","https://openalex.org/W2962784628","https://openalex.org/W2962801832","https://openalex.org/W2963216553","https://openalex.org/W2964034111","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2962784628","https://openalex.org/W2964308564","https://openalex.org/W2101105183","https://openalex.org/W2949888546","https://openalex.org/W2525778437","https://openalex.org/W2963876447","https://openalex.org/W2626778328","https://openalex.org/W2306852879","https://openalex.org/W1902237438","https://openalex.org/W1753482797","https://openalex.org/W2124807415","https://openalex.org/W2064675550","https://openalex.org/W2964121744","https://openalex.org/W2609011624","https://openalex.org/W6908809","https://openalex.org/W2950635152","https://openalex.org/W2613904329","https://openalex.org/W2563574619","https://openalex.org/W2418388682","https://openalex.org/W2100664567"],"abstract_inverted_index":{"Neural":[0],"machine":[1,94],"translation":[2,95],"has":[3],"recently":[4],"achieved":[5],"impressive":[6],"results,":[7],"while":[8],"using":[9],"little":[10],"in":[11,47,57,69],"the":[12,24,51,55,58,64,72],"way":[13],"of":[14,28,54,66,125],"external":[15],"linguistic":[16,35,108],"information.":[17],"In":[18,97],"this":[19],"paper":[20],"we":[21,105],"show":[22],"that":[23,107],"strong":[25],"learning":[26],"capability":[27],"neural":[29,93,127],"MT":[30,128],"models":[31],"does":[32],"not":[33],"make":[34],"features":[36,88,110],"redundant;":[37],"they":[38],"can":[39],"be":[40],"easily":[41],"incorporated":[42],"to":[43,62,71,89,115],"provide":[44],"further":[45],"improvements":[46],"performance.":[48],"We":[49,76],"generalize":[50],"embedding":[52],"layer":[53],"encoder":[56],"attentional":[59],"encoder--decoder":[60],"architecture":[61],"support":[63],"inclusion":[65],"arbitrary":[67],"features,":[68,79],"addition":[70],"baseline":[73],"word":[74],"feature.":[75],"add":[77],"morphological":[78],"part-of-speech":[80],"tags,":[81],"and":[82,91,102,120,136],"syntactic":[83],"dependency":[84],"labels":[85],"as":[86,132],"input":[87,109],"EnglishGerman,":[90],"English->Romanian":[92],"systems.":[96],"experiments":[98],"on":[99],"WMT16":[100],"training":[101],"test":[103],"sets,":[104],"find":[106],"improve":[111],"model":[112],"quality":[113],"according":[114],"three":[116],"metrics:":[117],"perplexity,":[118],"BLEU":[119],"CHRF3.":[121],"An":[122],"open-source":[123],"implementation":[124],"our":[126],"system":[129],"is":[130],"available,":[131],"are":[133],"sample":[134],"files":[135],"configurations.":[137]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":22},{"year":2017,"cited_by_count":16}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}