{"id":"https://openalex.org/W3202627914","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534370","title":"Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction","display_name":"Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3202627914","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534370","mag":"3202627914"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.09029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053698849","display_name":"Kosuke Nishida","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kosuke Nishida","raw_affiliation_strings":["NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110780218","display_name":"Kyosuke Nishida","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyosuke Nishida","raw_affiliation_strings":["NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008842415","display_name":"Itsumi Saito","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Itsumi Saito","raw_affiliation_strings":["NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104019809","display_name":"Sen Yoshida","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sen Yoshida","raw_affiliation_strings":["NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories, NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5053698849"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.6999,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76590562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"8"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9943000078201294,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9939000010490417,"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/interpretability","display_name":"Interpretability","score":0.9870445728302002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7470225691795349},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7253397703170776},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6503523588180542},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6284530758857727},{"id":"https://openalex.org/keywords/reading-comprehension","display_name":"Reading comprehension","score":0.5876826643943787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5822152495384216},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.5443488955497742},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5062583684921265},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.48504799604415894},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44848617911338806},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33249834179878235},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.0763024091720581}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9870445728302002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470225691795349},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7253397703170776},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6503523588180542},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6284530758857727},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.5876826643943787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5822152495384216},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.5443488955497742},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5062583684921265},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.48504799604415894},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44848617911338806},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33249834179878235},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0763024091720581},{"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2111.09029","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.09029","pdf_url":"https://arxiv.org/pdf/2111.09029","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.09029","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.09029","pdf_url":"https://arxiv.org/pdf/2111.09029","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W10548402","https://openalex.org/W2242818861","https://openalex.org/W2296701362","https://openalex.org/W2547875792","https://openalex.org/W2741263286","https://openalex.org/W2804897457","https://openalex.org/W2889453388","https://openalex.org/W2889787757","https://openalex.org/W2896457183","https://openalex.org/W2946364534","https://openalex.org/W2949227999","https://openalex.org/W2950618399","https://openalex.org/W2951328433","https://openalex.org/W2951862794","https://openalex.org/W2953163841","https://openalex.org/W2962790223","https://openalex.org/W2963029083","https://openalex.org/W2963159735","https://openalex.org/W2963233086","https://openalex.org/W2963323070","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963748441","https://openalex.org/W2963866616","https://openalex.org/W2970155250","https://openalex.org/W2970886003","https://openalex.org/W2988584128","https://openalex.org/W2997759614","https://openalex.org/W3008690242","https://openalex.org/W3015854960","https://openalex.org/W3017192286","https://openalex.org/W3035503910","https://openalex.org/W3097239661","https://openalex.org/W3099876468","https://openalex.org/W3100436891","https://openalex.org/W3105055324","https://openalex.org/W3155728828","https://openalex.org/W4299408792","https://openalex.org/W4385245566","https://openalex.org/W4389739628","https://openalex.org/W6697501834","https://openalex.org/W6718991148","https://openalex.org/W6729448088","https://openalex.org/W6739901393","https://openalex.org/W6750535842","https://openalex.org/W6755207826","https://openalex.org/W6763189663","https://openalex.org/W6763331066","https://openalex.org/W6769729683","https://openalex.org/W6770212421","https://openalex.org/W6774796078"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2082296339","https://openalex.org/W2161828220","https://openalex.org/W1972348076","https://openalex.org/W2083863157"],"abstract_inverted_index":{"Multi-hop":[0],"QA":[1],"with":[2,39],"annotated":[3],"supporting":[4,59],"facts,":[5],"which":[6],"is":[7],"the":[8,15,18,40,50,57,62,76,81,86,89,99,105,108,112,146,153,160],"task":[9],"of":[10,17,42,74,88,159],"reading":[11,31],"comprehension":[12,32],"(RC)":[13],"considering":[14,114],"interpretability":[16,106],"answer,":[19],"has":[20],"been":[21],"extensively":[22],"studied.":[23],"In":[24],"this":[25],"study,":[26],"we":[27,110],"define":[28],"an":[29,94],"interpretable":[30],"(IRC)":[33],"model":[34,38,48,69,131,135,148],"as":[35],"a":[36,117,121,133],"pipeline":[37,100,130],"capability":[41],"predicting":[43],"unanswerable":[44,71],"queries.":[45],"The":[46,67],"IRC":[47,68,147],"justifies":[49],"answer":[51,77],"prediction":[52,163],"by":[53],"establishing":[54],"consistency":[55],"between":[56,162],"predicted":[58],"facts":[60],"and":[61,107,165],"actual":[63],"rationale":[64],"for":[65,98,120],"interpretability.":[66,166],"detects":[70],"questions,":[72],"instead":[73],"outputting":[75],"forcibly":[78],"based":[79],"on":[80,136],"insufficient":[82],"information,":[83],"to":[84,152],"ensure":[85],"reliability":[87],"answer.":[90],"We":[91,124],"also":[92,143],"propose":[93],"end-to-end":[95,128],"training":[96],"method":[97],"RC":[101],"model.":[102],"To":[103],"evaluate":[104],"reliability,":[109],"conducted":[111],"experiments":[113],"unanswerability":[115],"in":[116,157],"multi-hop":[118],"question":[119],"given":[122],"passage.":[123],"show":[125,144],"that":[126,145],"our":[127,137],"trainable":[129],"outperformed":[132],"non-interpretable":[134,155],"modified":[138],"HotpotQA":[139],"dataset.":[140],"Experimental":[141],"results":[142,151],"achieves":[149],"comparable":[150],"previous":[154],"models":[156],"spite":[158],"trade-off":[161],"performance":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
