{"id":"https://openalex.org/W4389520451","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.605","title":"Semi-Structured Object Sequence Encoders","display_name":"Semi-Structured Object Sequence Encoders","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389520451","doi":"https://doi.org/10.18653/v1/2023.findings-emnlp.605"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.605","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2023.findings-emnlp.605","pdf_url":"https://aclanthology.org/2023.findings-emnlp.605.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2023.findings-emnlp.605.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000493089","display_name":"Rudra Murthy","orcid":"https://orcid.org/0000-0002-6236-1931"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rudra Murthy","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090412070","display_name":"Riyaz Ahmad Bhat","orcid":"https://orcid.org/0000-0002-8327-2882"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riyaz Bhat","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023638340","display_name":"Chulaka Gunasekara","orcid":"https://orcid.org/0000-0001-9272-3684"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chulaka Gunasekara","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009763686","display_name":"Siva Patel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siva Patel","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023448998","display_name":"Hui Wan","orcid":"https://orcid.org/0000-0001-9754-5469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hui Wan","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017064237","display_name":"Tejas I. Dhamecha","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162141","display_name":"Microsoft (India)","ror":"https://ror.org/04ww0w091","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210162141"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tejas Dhamecha","raw_affiliation_strings":["Microsoft India Development Center"],"affiliations":[{"raw_affiliation_string":"Microsoft India Development Center","institution_ids":["https://openalex.org/I4210162141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082114795","display_name":"Danish Contractor","orcid":"https://orcid.org/0000-0002-6843-1961"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danish Contractor","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048281388","display_name":"Marina Danilevsky","orcid":"https://orcid.org/0000-0003-2875-2442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marina Danilevsky","raw_affiliation_strings":["IBM Research AI"],"affiliations":[{"raw_affiliation_string":"IBM Research AI","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5000493089"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16684204,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"9026","last_page":"9039"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9994000196456909,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9994000196456909,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9984999895095825,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8355860114097595},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6898647546768188},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6726471781730652},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6485648155212402},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6353319883346558},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6144863963127136},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6091577410697937},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5542390942573547},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5533977746963501},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.5286111235618591},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4729604125022888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43644773960113525},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3208200931549072}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8355860114097595},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6898647546768188},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6726471781730652},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6485648155212402},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6353319883346558},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6144863963127136},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6091577410697937},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5542390942573547},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5533977746963501},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.5286111235618591},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4729604125022888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43644773960113525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3208200931549072},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","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/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2023.findings-emnlp.605","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2023.findings-emnlp.605","pdf_url":"https://aclanthology.org/2023.findings-emnlp.605.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2023.findings-emnlp.605","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2023.findings-emnlp.605","pdf_url":"https://aclanthology.org/2023.findings-emnlp.605.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":"Findings of the Association for Computational Linguistics: EMNLP 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389520451.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1682403713","https://openalex.org/W2047057213","https://openalex.org/W2060277733","https://openalex.org/W2424347275","https://openalex.org/W2549401308","https://openalex.org/W2754665629","https://openalex.org/W2895387432","https://openalex.org/W2962785754","https://openalex.org/W2962864421","https://openalex.org/W2963341956","https://openalex.org/W2963430933","https://openalex.org/W2971057144","https://openalex.org/W2991309414","https://openalex.org/W2998116985","https://openalex.org/W3015468748","https://openalex.org/W3049160591","https://openalex.org/W3098985395","https://openalex.org/W3109558947","https://openalex.org/W3153051631","https://openalex.org/W3155208531","https://openalex.org/W3157891451","https://openalex.org/W3158303960","https://openalex.org/W3160590016","https://openalex.org/W3173446720","https://openalex.org/W3173950477","https://openalex.org/W3174986053","https://openalex.org/W3177318507","https://openalex.org/W3188872815","https://openalex.org/W3200664681","https://openalex.org/W3203619751","https://openalex.org/W3204263062","https://openalex.org/W3212890323","https://openalex.org/W4205508242","https://openalex.org/W4221163895","https://openalex.org/W4226154561","https://openalex.org/W4233424333","https://openalex.org/W4281656839","https://openalex.org/W4285172793","https://openalex.org/W4288375142","https://openalex.org/W4297733535","https://openalex.org/W4320854709","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W1950940422","https://openalex.org/W4283822356","https://openalex.org/W2129146436","https://openalex.org/W2032507829","https://openalex.org/W2147282173"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,14,92],"explore":[4],"the":[5,19,124,135,181],"task":[6],"of":[7,21,31,46,54,56,87,137,180],"modeling":[8,64],"semi-structured":[9],"object":[10,112],"sequences;":[11],"in":[12],"particular,":[13],"focus":[15],"our":[16,158],"attention":[17,145],"on":[18,37,110,149],"problem":[20],"developing":[22],"a":[23,52,74,85,102,119,161,173,177],"structure-aware":[24],"input":[25],"representation":[26,86,175,179],"for":[27,143],"such":[28,32],"sequences.":[29],"Examples":[30],"data":[33,47,155],"include":[34],"user":[35],"activity":[36],"websites,":[38],"machine":[39],"logs,":[40],"and":[41,61,83,127,176],"many":[42],"others.":[43],"This":[44,105],"type":[45],"is":[48],"often":[49],"represented":[50],"as":[51,167,169],"sequence":[53,70],"sets":[55],"key-value":[57],"pairs":[58],"over":[59,90,95],"time":[60],"can":[62],"present":[63,128],"challenges":[65],"due":[66],"to":[67,100,108],"an":[68,129],"ever-increasing":[69],"length.":[71],"We":[72,117],"propose":[73],"two-part":[75],"approach,":[76],"which":[77],"first":[78],"considers":[79],"each":[80],"key":[81,98],"independently":[82],"encodes":[84],"its":[88],"values":[89],"time;":[91],"then":[93],"self-attend":[94],"these":[96],"value-aware":[97],"representations":[99],"accomplish":[101],"downstream":[103],"task.":[104],"allows":[106],"us":[107],"operate":[109],"longer":[111],"sequences":[113],"than":[114],"existing":[115],"methods.":[116],"introduce":[118],"novel":[120],"shared-attention-head":[121],"architecture":[122],"between":[123],"two":[125],"modules":[126,139],"innovative":[130],"training":[131,136],"schedule":[132],"that":[133,157],"interleaves":[134],"both":[138],"with":[140,164],"shared":[141],"weights":[142],"some":[144],"heads.":[146],"Our":[147],"experiments":[148],"multiple":[150],"prediction":[151],"tasks":[152],"using":[153],"real-world":[154],"demonstrate":[156],"approach":[159],"outperforms":[160],"unified":[162],"network":[163],"hierarchical":[165],"encoding,":[166],"well":[168],"other":[170],"methods":[171],"including":[172],"record-centric":[174],"flattened":[178],"sequence.":[182]},"counts_by_year":[],"updated_date":"2026-01-22T23:29:09.771500","created_date":"2025-10-10T00:00:00"}
