{"id":"https://openalex.org/W2740787117","doi":"https://doi.org/10.18653/v1/w17-2627","title":"Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning","display_name":"Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2740787117","doi":"https://doi.org/10.18653/v1/w17-2627","mag":"2740787117"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w17-2627","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w17-2627","pdf_url":"https://www.aclweb.org/anthology/W17-2627.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 2nd Workshop on Representation Learning for NLP","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W17-2627.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041145688","display_name":"\u00c7a\u01e7lar G\u00fcl\u00e7ehre","orcid":"https://orcid.org/0009-0003-4124-1687"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA","GB"],"is_corresponding":true,"raw_author_name":"Caglar Gulcehre","raw_affiliation_strings":["University of Montreal","Microsoft Research"],"affiliations":[{"raw_affiliation_string":"University of Montreal","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047262823","display_name":"Francis Dutil","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA","GB"],"is_corresponding":false,"raw_author_name":"Francis Dutil","raw_affiliation_strings":["Microsoft Research","University of Montreal"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"University of Montreal","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072931308","display_name":"Adam Trischler","orcid":"https://orcid.org/0000-0003-1118-8224"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA","GB"],"is_corresponding":false,"raw_author_name":"Adam Trischler","raw_affiliation_strings":["University of Montreal","Microsoft Research"],"affiliations":[{"raw_affiliation_string":"University of Montreal","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086198262","display_name":"Yoshua Bengio","orcid":"https://orcid.org/0000-0002-9322-3515"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA","GB"],"is_corresponding":false,"raw_author_name":"Yoshua Bengio","raw_affiliation_strings":["Microsoft Research","University of Montreal"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"University of Montreal","institution_ids":["https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041145688"],"corresponding_institution_ids":["https://openalex.org/I4210164937","https://openalex.org/I70931966"],"apc_list":null,"apc_paid":null,"fwci":1.3652,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.8586765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"228","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9998999834060669,"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":0.9915000200271606,"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":0.8230352401733398},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.6854131817817688},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.6198776364326477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.586784839630127},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5803706049919128},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5526573657989502},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.5177468657493591},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.5165397524833679},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.505695104598999},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.49730899930000305},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46707087755203247},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3735426068305969}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8230352401733398},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.6854131817817688},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.6198776364326477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.586784839630127},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5803706049919128},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5526573657989502},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5177468657493591},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.5165397524833679},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.505695104598999},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.49730899930000305},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46707087755203247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3735426068305969},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w17-2627","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w17-2627","pdf_url":"https://www.aclweb.org/anthology/W17-2627.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 2nd Workshop on Representation Learning for NLP","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w17-2627","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w17-2627","pdf_url":"https://www.aclweb.org/anthology/W17-2627.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 2nd Workshop on Representation Learning for NLP","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5299999713897705,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2740787117.pdf","grobid_xml":"https://content.openalex.org/works/W2740787117.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2119717200","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2242818861","https://openalex.org/W2311921240","https://openalex.org/W2442341664","https://openalex.org/W2487501366","https://openalex.org/W2531207078","https://openalex.org/W2547875792","https://openalex.org/W2548228487","https://openalex.org/W2583687993","https://openalex.org/W2952165242","https://openalex.org/W2962784628","https://openalex.org/W2962944953","https://openalex.org/W2963251942","https://openalex.org/W2963324947","https://openalex.org/W2963830168","https://openalex.org/W2964199361","https://openalex.org/W2964308564","https://openalex.org/W2964335273","https://openalex.org/W2964352247","https://openalex.org/W4297827933","https://openalex.org/W4394666973"],"related_works":["https://openalex.org/W4285277090","https://openalex.org/W4327738859","https://openalex.org/W4251972423","https://openalex.org/W1503216044","https://openalex.org/W2348722996","https://openalex.org/W2393609567","https://openalex.org/W2369369044","https://openalex.org/W2898767136","https://openalex.org/W2883671469","https://openalex.org/W2728761353"],"abstract_inverted_index":{"We":[0,14,106],"investigate":[1],"the":[2,27,40,65],"integration":[3],"of":[4,50],"a":[5,16,35,48,55,79],"planning":[6,84],"mechanism":[7,68],"into":[8],"an":[9],"encoder-decoder":[10],"architecture":[11,82],"with":[12,85,103,120],"attention.":[13],"develop":[15],"model":[17,99,110],"that":[18,58,89,108,126],"can":[19,90],"plan":[20],"ahead":[21],"when":[22],"it":[23],"computes":[24,124],"alignments":[25,53,125],"between":[26],"source":[28],"and":[29,54,75,123],"target":[30],"sequences":[31],"not":[32],"only":[33],"for":[34,39,83],"single":[36],"time-step,":[37],"but":[38],"next":[41],"k":[42],"timesteps":[43],"as":[44],"well":[45],"by":[46,71],"constructing":[47],"matrix":[49],"proposed":[51,98],"future":[52],"commitment":[56],"vector":[57],"governs":[59],"whether":[60],"to":[61],"follow":[62],"or":[63],"recompute":[64],"plan.":[66],"This":[67],"is":[69,100],"inspired":[70],"strategic":[72],"attentive":[73],"reader":[74],"writer":[76],"(STRAW)":[77],"model,":[78],"recent":[80],"neural":[81],"hierarchical":[86],"reinforcement":[87],"learning":[88],"also":[91],"learn":[92],"higher":[93],"level":[94],"temporal":[95],"abstractions.":[96],"Our":[97],"end-to-end":[101],"trainable":[102],"differentiable":[104],"operations.":[105],"show":[107],"our":[109],"outperforms":[111],"strong":[112],"baselines":[113],"on":[114],"character-level":[115],"translation":[116],"task":[117],"from":[118],"WMT'15":[119],"less":[121],"parameters":[122],"are":[127],"qualitatively":[128],"intuitive.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
