{"id":"https://openalex.org/W3012872813","doi":"https://doi.org/10.1145/3366423.3380034","title":"Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification","display_name":"Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012872813","doi":"https://doi.org/10.1145/3366423.3380034","mag":"3012872813"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380034","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 Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380034","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037995911","display_name":"Yaowei Zheng","orcid":"https://orcid.org/0000-0001-6028-8032"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaowei Zheng","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027015677","display_name":"Richong Zhang","orcid":"https://orcid.org/0000-0002-1207-0300"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Richong Zhang","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073554817","display_name":"Suyuchen Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suyuchen Wang","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089184655","display_name":"Samuel Mensah","orcid":"https://orcid.org/0000-0003-0779-5574"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Samuel Mensah","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004793184","display_name":"Yongyi Mao","orcid":"https://orcid.org/0000-0001-5298-5778"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yongyi Mao","raw_affiliation_strings":["University of Ottawa"],"affiliations":[{"raw_affiliation_string":"University of Ottawa","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037995911"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.823,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78400152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2754","last_page":"2760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T12676","display_name":"Machine Learning and ELM","score":0.9932000041007996,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9923999905586243,"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/transfer-of-learning","display_name":"Transfer of learning","score":0.856869637966156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8085681200027466},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7430160045623779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6875115633010864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6605935096740723},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6424059867858887},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5502113699913025},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5131381154060364},{"id":"https://openalex.org/keywords/inductive-transfer","display_name":"Inductive transfer","score":0.46599280834198},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.44388291239738464},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.43734824657440186},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.4138444662094116},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07267814874649048}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.856869637966156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8085681200027466},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7430160045623779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6875115633010864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6605935096740723},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6424059867858887},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5502113699913025},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5131381154060364},{"id":"https://openalex.org/C77075516","wikidata":"https://www.wikidata.org/wiki/Q6027324","display_name":"Inductive transfer","level":5,"score":0.46599280834198},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.44388291239738464},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.43734824657440186},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.4138444662094116},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07267814874649048},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C188888258","wikidata":"https://www.wikidata.org/wiki/Q7353390","display_name":"Robot learning","level":4,"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380034","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 Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380034","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 Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1533861849","https://openalex.org/W1848260265","https://openalex.org/W1988790447","https://openalex.org/W2025198378","https://openalex.org/W2025413459","https://openalex.org/W2027731328","https://openalex.org/W2030866871","https://openalex.org/W2075728230","https://openalex.org/W2105103433","https://openalex.org/W2122838776","https://openalex.org/W2125918842","https://openalex.org/W2159526726","https://openalex.org/W2161381512","https://openalex.org/W2163568299","https://openalex.org/W2165698076","https://openalex.org/W2250966211","https://openalex.org/W2251124635","https://openalex.org/W2251292973","https://openalex.org/W2251648804","https://openalex.org/W2251732921","https://openalex.org/W2252057809","https://openalex.org/W2295479927","https://openalex.org/W2308486447","https://openalex.org/W2465978385","https://openalex.org/W2515059321","https://openalex.org/W2562607067","https://openalex.org/W2581851997","https://openalex.org/W2593768305","https://openalex.org/W2597243853","https://openalex.org/W2625324377","https://openalex.org/W2739748921","https://openalex.org/W2757541972","https://openalex.org/W2771083582","https://openalex.org/W2788610610","https://openalex.org/W2799007071","https://openalex.org/W2891778157","https://openalex.org/W2903110172","https://openalex.org/W2904893967","https://openalex.org/W2949161734","https://openalex.org/W2949821452","https://openalex.org/W2950404230","https://openalex.org/W2951670162","https://openalex.org/W2953029945","https://openalex.org/W2962717763","https://openalex.org/W2963024368","https://openalex.org/W2963168371","https://openalex.org/W2963240575","https://openalex.org/W2963303028","https://openalex.org/W2963428430","https://openalex.org/W2963463240","https://openalex.org/W2963826681","https://openalex.org/W2964094426","https://openalex.org/W2964098749","https://openalex.org/W2964199361","https://openalex.org/W4248437541","https://openalex.org/W7062423369"],"related_works":["https://openalex.org/W3016888008","https://openalex.org/W4387770285","https://openalex.org/W3022215768","https://openalex.org/W2165698076","https://openalex.org/W2086210685","https://openalex.org/W4387183713","https://openalex.org/W4382138864","https://openalex.org/W2821676139","https://openalex.org/W3043695725","https://openalex.org/W3135975972"],"abstract_inverted_index":{"Supervised":[0],"learning":[1,21,44,73],"relies":[2],"heavily":[3],"on":[4,88,126,142],"readily":[5],"available":[6],"labelled":[7,29],"data":[8,30],"to":[9,38,40,47,52,59],"infer":[10],"an":[11],"effective":[12],"classification":[13,118],"function.":[14],"However,":[15],"proposed":[16],"methods":[17,74,137],"under":[18],"the":[19,26,109,120,133,143],"supervised":[20],"paradigm":[22],"are":[23,34,85],"faced":[24],"with":[25],"scarcity":[27],"of":[28,111,135],"within":[31],"domains,":[32],"and":[33,64,79,91,96,99,113],"not":[35],"generalized":[36],"enough":[37],"adapt":[39],"other":[41],"tasks.":[42,65],"Transfer":[43,77,82],"has":[45],"proved":[46],"be":[48,60,101],"a":[49,104],"worthy":[50],"choice":[51],"address":[53],"these":[54],"issues,":[55],"by":[56],"allowing":[57],"knowledge":[58],"shared":[61],"across":[62],"domains":[63],"In":[66],"this":[67],"paper,":[68],"we":[69,130],"propose":[70],"two":[71],"transfer":[72,95],"Anchored":[75],"Model":[76],"(AMT)":[78],"Soft":[80],"Instance":[81],"(SIT),":[83],"which":[84],"both":[86,136],"based":[87],"multi-task":[89],"learning,":[90],"account":[92],"for":[93,115],"model":[94],"instance":[97],"transfer,":[98],"can":[100],"combined":[102],"into":[103],"common":[105],"framework.":[106],"We":[107],"demonstrate":[108],"effectiveness":[110],"AMT":[112],"SIT":[114],"aspect-level":[116],"sentiment":[117],"showing":[119],"competitive":[121],"performance":[122,141],"against":[123],"baseline":[124],"models":[125],"benchmark":[127],"datasets.":[128],"Interestingly,":[129],"show":[131],"that":[132],"integration":[134],"AMT+SIT":[138],"achieves":[139],"state-of-the-art":[140],"same":[144],"task.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
