{"id":"https://openalex.org/W2982756572","doi":"https://doi.org/10.18653/v1/d19-5823","title":"Extractive NarrativeQA with Heuristic Pre-Training","display_name":"Extractive NarrativeQA with Heuristic Pre-Training","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2982756572","doi":"https://doi.org/10.18653/v1/d19-5823","mag":"2982756572"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d19-5823","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5823","pdf_url":"https://www.aclweb.org/anthology/D19-5823.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 Machine Reading for Question Answering","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D19-5823.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025156794","display_name":"Lea Frermann","orcid":"https://orcid.org/0000-0002-9712-1188"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Lea Frermann","raw_affiliation_strings":["School of Computing and Information Systems The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5025156794"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.867,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81418796,"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":"172","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9908000230789185,"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":0.8419278860092163},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7366045713424683},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.653977632522583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6517937183380127},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6260799765586853},{"id":"https://openalex.org/keywords/scripting-language","display_name":"Scripting language","score":0.5779843926429749},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5686300992965698},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5547312498092651},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5429477691650391},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.541100263595581},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4956620931625366},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4575982689857483},{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.4394264221191406},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.422030508518219}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8419278860092163},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7366045713424683},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.653977632522583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6517937183380127},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6260799765586853},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.5779843926429749},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5686300992965698},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5547312498092651},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5429477691650391},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.541100263595581},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4956620931625366},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4575982689857483},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.4394264221191406},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.422030508518219},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d19-5823","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5823","pdf_url":"https://www.aclweb.org/anthology/D19-5823.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 Machine Reading for Question Answering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d19-5823","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5823","pdf_url":"https://www.aclweb.org/anthology/D19-5823.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 Machine Reading for Question Answering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982756572.pdf","grobid_xml":"https://content.openalex.org/works/W2982756572.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W131411689","https://openalex.org/W2095881835","https://openalex.org/W2101105183","https://openalex.org/W2123301721","https://openalex.org/W2551396370","https://openalex.org/W2606974598","https://openalex.org/W2794557536","https://openalex.org/W2890498499","https://openalex.org/W2891850907","https://openalex.org/W2896457183","https://openalex.org/W2896780650","https://openalex.org/W2912924812","https://openalex.org/W2949210751","https://openalex.org/W2950695840","https://openalex.org/W2963323070","https://openalex.org/W2963341956","https://openalex.org/W2963595025","https://openalex.org/W2963672599","https://openalex.org/W2963748441","https://openalex.org/W2963866616","https://openalex.org/W2963963993"],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W2046765700","https://openalex.org/W3204607391","https://openalex.org/W2964413124","https://openalex.org/W4388937922","https://openalex.org/W3113264705","https://openalex.org/W4287644835","https://openalex.org/W3092281475","https://openalex.org/W3098003361"],"abstract_inverted_index":{"Although":[0],"advances":[1],"in":[2],"neural":[3],"architectures":[4],"for":[5,87,103],"NLP":[6],"problems":[7],"and":[8,18,90,144,149],"unsupervised":[9],"pre-training":[10],"led":[11],"to":[12,72,116],"impressive":[13],"improvements":[14],"on":[15,122,132],"question":[16,37],"answering":[17,38],"natural":[19],"language":[20],"inference,":[21],"reasoning":[22],"over":[23],"long":[24],"texts":[25],"still":[26],"poses":[27],"a":[28,61,151],"great":[29],"challenge.":[30],"Here,":[31],"we":[32],"consider":[33],"the":[34,51,66,74,119,133,138,145],"task":[35],"of":[36,65,78,118,141],"from":[39],"full":[40],"narratives":[41],"(e.g.,":[42],"books":[43],"or":[44,47],"movie":[45],"scripts),":[46],"their":[48],"summaries,":[49],"tackling":[50],"NarrativeQA":[52],"challenge":[53],"(NQA;":[54],"Kocisky":[55],"et":[56],"al.":[57],"(":[58],"We":[59,84,98,110,136],"introduce":[60],"heuristic":[62],"extractive":[63,146],"version":[64],"data":[67,96],"set,":[68],"which":[69],"allows":[70],"us":[71],"approach":[73],"more":[75],"feasible":[76],"problem":[77],"answer":[79,91],"extraction":[80],"(rather":[81],"than":[82],"generation).":[83],"develop":[85],"models":[86],"passage":[88],"retrieval":[89],"span":[92],"prediction":[93],"using":[94],"this":[95],"set.":[97],"use":[99],"pre-trained":[100],"BERT":[101,142],"embeddings":[102,143],"injecting":[104],"prior":[105],"knowledge":[106],"into":[107],"our":[108,113,128],"system.":[109],"show":[111],"that":[112],"setup":[114],"leads":[115],"state":[117],"art":[120],"performance":[121],"summary-level":[123],"QA.":[124],"On":[125],"narrativelevel":[126],"QA,":[127],"model":[129,147],"performs":[130],"competitively":[131],"METEOR":[134],"metric.":[135],"analyze":[137],"relative":[139],"contributions":[140],"setup,":[148],"provide":[150],"detailed":[152],"error":[153],"analysis.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}