{"id":"https://openalex.org/W2986728023","doi":"https://doi.org/10.18653/v1/k19-1065","title":"Evidence Sentence Extraction for Machine Reading Comprehension","display_name":"Evidence Sentence Extraction for Machine Reading Comprehension","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2986728023","doi":"https://doi.org/10.18653/v1/k19-1065","mag":"2986728023"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k19-1065","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1065","pdf_url":"https://www.aclweb.org/anthology/K19-1065.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/K19-1065.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009768180","display_name":"Hai Wang","orcid":"https://orcid.org/0000-0003-2789-9530"},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hai Wang","raw_affiliation_strings":["Toyota Technological Institute at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I160992636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101834699","display_name":"Dian Yu","orcid":"https://orcid.org/0000-0002-8583-8931"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dian Yu","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053739372","display_name":"Kai Sun","orcid":"https://orcid.org/0000-0003-2281-5051"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Sun","raw_affiliation_strings":["Cornell, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088339142","display_name":"Jianshu Chen","orcid":"https://orcid.org/0000-0001-8216-2756"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianshu Chen","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034476404","display_name":"Dong Yu","orcid":"https://orcid.org/0000-0003-0520-6844"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Yu","raw_affiliation_strings":["Tencent AI Lab, Bellevue, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033089246","display_name":"David McAllester","orcid":null},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David McAllester","raw_affiliation_strings":["Toyota Technological Institute at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I160992636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023802054","display_name":"Dan Roth","orcid":"https://orcid.org/0009-0002-1447-5173"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dan Roth","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":47,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"696","last_page":"707"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T13629","display_name":"Text Readability and Simplification","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":0.825973391532898},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7406277060508728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6625734567642212},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6390161514282227},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5807368755340576},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5078232884407043},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.48865631222724915},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.48658668994903564},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.473137766122818},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45711469650268555},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41674935817718506},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3739687204360962},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.15847569704055786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.825973391532898},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7406277060508728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6625734567642212},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6390161514282227},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5807368755340576},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5078232884407043},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.48865631222724915},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.48658668994903564},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.473137766122818},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45711469650268555},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41674935817718506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3739687204360962},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.15847569704055786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/k19-1065","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1065","pdf_url":"https://www.aclweb.org/anthology/K19-1065.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/k19-1065","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k19-1065","pdf_url":"https://www.aclweb.org/anthology/K19-1065.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 23rd Conference on Computational Natural Language Learning (CoNLL)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2986728023.pdf","grobid_xml":"https://content.openalex.org/works/W2986728023.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W1840435438","https://openalex.org/W1977970897","https://openalex.org/W2099813784","https://openalex.org/W2122311631","https://openalex.org/W2125436846","https://openalex.org/W2126209950","https://openalex.org/W2170947027","https://openalex.org/W2194775991","https://openalex.org/W2250308783","https://openalex.org/W2250432970","https://openalex.org/W2250595585","https://openalex.org/W2250770256","https://openalex.org/W2251329024","https://openalex.org/W2251355301","https://openalex.org/W2341790067","https://openalex.org/W2561529111","https://openalex.org/W2575101981","https://openalex.org/W2604685013","https://openalex.org/W2606964149","https://openalex.org/W2740929653","https://openalex.org/W2741263286","https://openalex.org/W2753329127","https://openalex.org/W2759477115","https://openalex.org/W2760268776","https://openalex.org/W2767693128","https://openalex.org/W2783905278","https://openalex.org/W2788285996","https://openalex.org/W2788496822","https://openalex.org/W2799081691","https://openalex.org/W2799187742","https://openalex.org/W2804897457","https://openalex.org/W2806055002","https://openalex.org/W2869186267","https://openalex.org/W2886390638","https://openalex.org/W2888041867","https://openalex.org/W2892163801","https://openalex.org/W2896457183","https://openalex.org/W2912904516","https://openalex.org/W2923014074","https://openalex.org/W2952467640","https://openalex.org/W2962727366","https://openalex.org/W2962800603","https://openalex.org/W2962828804","https://openalex.org/W2962985038","https://openalex.org/W2963159735","https://openalex.org/W2963301888","https://openalex.org/W2963310665","https://openalex.org/W2963323070","https://openalex.org/W2963341956","https://openalex.org/W2963363070","https://openalex.org/W2963403868","https://openalex.org/W2963547127","https://openalex.org/W2963748441","https://openalex.org/W2963769536","https://openalex.org/W2963846996","https://openalex.org/W2963918774","https://openalex.org/W2963961878","https://openalex.org/W2963963993","https://openalex.org/W2964151654","https://openalex.org/W2964212550","https://openalex.org/W2964223283","https://openalex.org/W2964267515","https://openalex.org/W4288614645","https://openalex.org/W4298149550","https://openalex.org/W4322422052","https://openalex.org/W4385245566","https://openalex.org/W4394666973"],"related_works":["https://openalex.org/W2374250903","https://openalex.org/W2487056937","https://openalex.org/W1546413948","https://openalex.org/W42072456","https://openalex.org/W2263832889","https://openalex.org/W2243884323","https://openalex.org/W2047973478","https://openalex.org/W4295532600","https://openalex.org/W2089057551","https://openalex.org/W1976369278"],"abstract_inverted_index":{"Remarkable":[0],"success":[1],"has":[2],"been":[3],"achieved":[4],"in":[5],"the":[6,25,45,52],"last":[7],"few":[8],"years":[9],"on":[10,36],"some":[11],"limited":[12],"machine":[13],"reading":[14],"comprehension":[15],"(MRC)":[16],"tasks.":[17],"However,":[18],"it":[19],"is":[20],"still":[21],"difficult":[22],"to":[23],"interpret":[24],"predictions":[26],"of":[27,47,54],"existing":[28],"MRC":[29,49],"models.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34],"focus":[35],"extracting":[37],"evidence":[38],"sentences":[39],"that":[40],"can":[41],"explain":[42],"or":[43],"support":[44],"answers":[46],"multiplechoice":[48],"tasks,":[50],"where":[51],"majority":[53],"answer":[55],"options":[56],"cannot":[57],"be":[58],"directly":[59],"extracted":[60],"from":[61],"reference":[62],"documents.":[63]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}