{"id":"https://openalex.org/W3034300118","doi":"https://doi.org/10.1145/3394486.3403047","title":"Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach","display_name":"Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3034300118","doi":"https://doi.org/10.1145/3394486.3403047","mag":"3034300118"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403047","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403047","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403047","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403047","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055007304","display_name":"Qifan Wang","orcid":"https://orcid.org/0000-0002-5304-7975"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qifan Wang","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051702877","display_name":"Yang Li","orcid":"https://orcid.org/0000-0001-7808-5524"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Yang","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057677675","display_name":"Bhargav Kanagal","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhargav Kanagal","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088663909","display_name":"Sumit Sanghai","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sumit Sanghai","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034629063","display_name":"D. Sivakumar","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Sivakumar","raw_affiliation_strings":["Google Research, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030341490","display_name":"Bin Shu","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Shu","raw_affiliation_strings":["Google, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076454839","display_name":"Zac Yu","orcid":"https://orcid.org/0000-0002-2452-5034"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zac Yu","raw_affiliation_strings":["Google, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020496646","display_name":"Jon Elsas","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jon Elsas","raw_affiliation_strings":["Google, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5055007304"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":6.7606,"has_fulltext":true,"cited_by_count":75,"citation_normalized_percentile":{"value":0.97415731,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"47","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T11550","display_name":"Text and Document Classification Technologies","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"}},{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/generalizability-theory","display_name":"Generalizability theory","score":0.8081963062286377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7746714949607849},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6794853210449219},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6566658020019531},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6033858060836792},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5631961226463318},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.5148705244064331},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46361666917800903},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4576278328895569},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.44686031341552734},{"id":"https://openalex.org/keywords/variable-and-attribute","display_name":"Variable and attribute","score":0.43509453535079956},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4311729371547699},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.43031707406044006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41898220777511597},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4051018953323364},{"id":"https://openalex.org/keywords/attribute-domain","display_name":"Attribute domain","score":0.2153787612915039},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13386204838752747},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1130569577217102},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07282039523124695},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.06994518637657166}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8081963062286377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7746714949607849},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6794853210449219},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6566658020019531},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6033858060836792},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5631961226463318},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.5148705244064331},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46361666917800903},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4576278328895569},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.44686031341552734},{"id":"https://openalex.org/C12692103","wikidata":"https://www.wikidata.org/wiki/Q113312","display_name":"Variable and attribute","level":4,"score":0.43509453535079956},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4311729371547699},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.43031707406044006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41898220777511597},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4051018953323364},{"id":"https://openalex.org/C75814411","wikidata":"https://www.wikidata.org/wiki/Q4818714","display_name":"Attribute domain","level":3,"score":0.2153787612915039},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13386204838752747},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1130569577217102},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07282039523124695},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.06994518637657166},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403047","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403047","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403047","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403047","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403047","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403047","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034300118.pdf","grobid_xml":"https://content.openalex.org/works/W3034300118.grobid-xml"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W70399244","https://openalex.org/W172829878","https://openalex.org/W1522989131","https://openalex.org/W1566289585","https://openalex.org/W1604644367","https://openalex.org/W1940872118","https://openalex.org/W1969221592","https://openalex.org/W1982982698","https://openalex.org/W2020278455","https://openalex.org/W2024105308","https://openalex.org/W2033047024","https://openalex.org/W2064675550","https://openalex.org/W2132667892","https://openalex.org/W2140636749","https://openalex.org/W2147458937","https://openalex.org/W2153579005","https://openalex.org/W2158899491","https://openalex.org/W2173361515","https://openalex.org/W2250518897","https://openalex.org/W2250521169","https://openalex.org/W2293453011","https://openalex.org/W2296045323","https://openalex.org/W2296283641","https://openalex.org/W2406945108","https://openalex.org/W2473930607","https://openalex.org/W2511827830","https://openalex.org/W2516936824","https://openalex.org/W2523060838","https://openalex.org/W2604259521","https://openalex.org/W2617242334","https://openalex.org/W2626778328","https://openalex.org/W2745153524","https://openalex.org/W2757732772","https://openalex.org/W2765238425","https://openalex.org/W2775521672","https://openalex.org/W2798341154","https://openalex.org/W2805173585","https://openalex.org/W2896457183","https://openalex.org/W2912924812","https://openalex.org/W2937055361","https://openalex.org/W2950133940","https://openalex.org/W2950808302","https://openalex.org/W2950813464","https://openalex.org/W2951865668","https://openalex.org/W2954936423","https://openalex.org/W2962881743","https://openalex.org/W2962902328","https://openalex.org/W2962982640","https://openalex.org/W2963020213","https://openalex.org/W2963499153","https://openalex.org/W2963625095","https://openalex.org/W2963748441","https://openalex.org/W2963959597","https://openalex.org/W2964121744","https://openalex.org/W2982150889","https://openalex.org/W2997617958","https://openalex.org/W2998369048","https://openalex.org/W6631190155","https://openalex.org/W6640362995","https://openalex.org/W6713647626","https://openalex.org/W6726232819","https://openalex.org/W6739901393","https://openalex.org/W6746994340","https://openalex.org/W6948274451"],"related_works":["https://openalex.org/W2352298027","https://openalex.org/W4319940250","https://openalex.org/W842810586","https://openalex.org/W2092919065","https://openalex.org/W4236762297","https://openalex.org/W3138801416","https://openalex.org/W2369351710","https://openalex.org/W2594363579","https://openalex.org/W2169232658","https://openalex.org/W2444550338"],"abstract_inverted_index":{"Attribute":[0],"value":[1,42],"extraction":[2,43,83],"refers":[3],"to":[4,84],"the":[5],"task":[6],"of":[7,10,13,61],"identifying":[8],"values":[9],"an":[11,20],"attribute":[12,41,53,70],"interest":[14],"from":[15],"product":[16],"information.":[17],"It":[18],"is":[19],"important":[21],"research":[22,78],"topic":[23],"which":[24,63],"has":[25,79],"been":[26],"widely":[27],"studied":[28],"in":[29,39,72],"e-Commerce":[30],"and":[31,46,55],"relation":[32],"learning.":[33],"There":[34],"are":[35,64],"two":[36],"main":[37],"limitations":[38],"existing":[40,49],"methods:":[44],"scalability":[45],"generalizability.":[47],"Most":[48],"methods":[50],"treat":[51],"each":[52,60],"independently":[54],"build":[56],"separate":[57],"models":[58],"for":[59,67],"them,":[62],"not":[65],"suitable":[66],"large":[68],"scale":[69],"systems":[71],"real-world":[73],"applications.":[74],"Moreover,":[75],"very":[76],"limited":[77],"focused":[80],"on":[81],"generalizing":[82],"new":[85],"attributes.":[86]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":10}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}