{"id":"https://openalex.org/W2895497338","doi":"https://doi.org/10.1145/3209280.3236023","title":"Can Deep Learning Compensate for a Shallow Evaluation?","display_name":"Can Deep Learning Compensate for a Shallow Evaluation?","publication_year":2018,"publication_date":"2018-08-28","ids":{"openalex":"https://openalex.org/W2895497338","doi":"https://doi.org/10.1145/3209280.3236023","mag":"2895497338"},"language":"en","primary_location":{"id":"doi:10.1145/3209280.3236023","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209280.3236023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Symposium on Document Engineering 2018","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052428595","display_name":"Gerald Penn","orcid":"https://orcid.org/0000-0003-3553-8305"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Gerald Penn","raw_affiliation_strings":["University of Toronto, Toronto, Ontario"],"affiliations":[{"raw_affiliation_string":"University of Toronto, Toronto, Ontario","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052428595"],"corresponding_institution_ids":["https://openalex.org/I185261750"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11044525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.6585999727249146,"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":0.6585999727249146,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.5817999839782715,"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/deep-learning","display_name":"Deep learning","score":0.769787073135376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7594608664512634},{"id":"https://openalex.org/keywords/the-renaissance","display_name":"The Renaissance","score":0.7347359657287598},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5459002256393433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5258767604827881},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4475814998149872},{"id":"https://openalex.org/keywords/natural","display_name":"Natural (archaeology)","score":0.4311034679412842},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42987245321273804},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.1172834038734436},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.07670781016349792}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.769787073135376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7594608664512634},{"id":"https://openalex.org/C52069626","wikidata":"https://www.wikidata.org/wiki/Q4692","display_name":"The Renaissance","level":2,"score":0.7347359657287598},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5459002256393433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5258767604827881},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4475814998149872},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.4311034679412842},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42987245321273804},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.1172834038734436},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.07670781016349792},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C52119013","wikidata":"https://www.wikidata.org/wiki/Q50637","display_name":"Art history","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3209280.3236023","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209280.3236023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Symposium on Document Engineering 2018","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8399999737739563,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4210998828","https://openalex.org/W2564674284","https://openalex.org/W2991835344","https://openalex.org/W1586974227","https://openalex.org/W3199681743","https://openalex.org/W2093204811","https://openalex.org/W4386903674","https://openalex.org/W4226226396","https://openalex.org/W3153750606","https://openalex.org/W4308854837"],"abstract_inverted_index":{"The":[0],"last":[1],"ten":[2],"years":[3],"have":[4,86],"witnessed":[5],"an":[6],"enormous":[7],"increase":[8],"in":[9,61,97,102],"the":[10,40,52,58,70,81,92,98],"application":[11],"of":[12,51,66,80],"\"deep":[13],"learning\"":[14],"methods":[15],"to":[16,29],"both":[17],"spoken":[18],"and":[19,37],"textual":[20],"natural":[21],"language":[22,35],"processing.":[23],"Have":[24],"they":[25],"helped?":[26],"With":[27],"respect":[28],"some":[30],"well-defined":[31],"tasks":[32],"such":[33],"as":[34],"modelling":[36],"acoustic":[38],"modelling,":[39],"answer":[41,71],"is":[42,72],"most":[43],"certainly":[44],"affirmative,":[45],"but":[46],"those":[47],"are":[48,56],"mere":[49],"components":[50],"real":[53,68],"applications":[54],"that":[55,74,85,100],"driving":[57],"increasing":[59],"interest":[60],"our":[62],"field.":[63],"In":[64],"many":[65],"these":[67],"applications,":[69],"surprisingly":[73],"we":[75],"cannot":[76],"be":[77],"certain":[78],"because":[79],"shambolic":[82],"evaluation":[83],"standards":[84],"been":[87],"commonplace":[88],"---":[89,96],"long":[90],"before":[91],"deep":[93],"learning":[94],"renaissance":[95],"communities":[99],"specialized":[101],"advancing":[103],"them.":[104]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
