{"id":"https://openalex.org/W2995628494","doi":null,"title":"Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension","display_name":"Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension","publication_year":2020,"publication_date":"2020-04-30","ids":{"openalex":"https://openalex.org/W2995628494","mag":"2995628494"},"language":"en","primary_location":{"id":"mag:2995628494","doi":null,"is_oa":false,"landing_page_url":"https://openreview.net/pdf?id=ryxjnREFwH","pdf_url":null,"source":{"id":"https://openalex.org/S4306419637","display_name":"International Conference on Learning Representations","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Learning Representations","raw_type":null},"volume":null,"type":"conference-paper","indexed_in":[],"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/A5101977008","display_name":"Xinyun Chen","orcid":"https://orcid.org/0009-0001-9700-8951","observed_orcids":["https://orcid.org/0009-0001-9700-8951"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyun Chen","raw_affiliation_strings":["University of California, Berkeley, Berkeley, United States"],"raw_orcid":"https://orcid.org/0009-0001-9700-8951","affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, United States","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101957211","display_name":"Liang Chen","orcid":"https://orcid.org/0000-0002-3149-0239","observed_orcids":["https://orcid.org/0000-0002-3149-0239"]},"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":"Liang Chen","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"raw_orcid":"https://orcid.org/0000-0002-3149-0239","affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045729519","display_name":"Adams Wei Yu","orcid":null,"observed_orcids":[]},"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":"Adams Wei Yu","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061512999","display_name":"Denny Zhou","orcid":null,"observed_orcids":[]},"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":"Denny Zhou","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019426968","display_name":"Dawn Xiaodong Song","orcid":"https://orcid.org/0000-0001-9745-6802","observed_orcids":["https://orcid.org/0000-0001-9745-6802"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawn Song","raw_affiliation_strings":["University of California, Berkeley, Berkeley, United States"],"raw_orcid":"https://orcid.org/0000-0001-9745-6802","affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, United States","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088551093","display_name":"Quoc Viet Le","orcid":"https://orcid.org/0000-0002-1087-2844","observed_orcids":["https://orcid.org/0000-0002-1087-2844"]},"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":"Quoc V. Le","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"raw_orcid":"https://orcid.org/0000-0002-1087-2844","affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.0092,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.97239056,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9179999828338623,"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":0.9179999828338623,"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":0.01679999940097332,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.01549999974668026,"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/neuro-symbolic-learning","display_name":"neuro-symbolic learning","score":0.6710000038146973},{"id":"https://openalex.org/keywords/reading-comprehension","display_name":"reading comprehension","score":0.9169999957084656},{"id":"https://openalex.org/keywords/program-synthesis","display_name":"program synthesis","score":0.6340000033378601},{"id":"https://openalex.org/keywords/compositional-reasoning","display_name":"compositional reasoning","score":0.6570000052452087},{"id":"https://openalex.org/keywords/weakly-supervised-learning","display_name":"weakly supervised learning","score":0.5809999704360962},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.6449999809265137},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7570000290870667},{"id":"https://openalex.org/keywords/thresholding","display_name":"thresholding","score":0.7369999885559082},{"id":"https://openalex.org/keywords/interpretable-representations","display_name":"interpretable representations","score":0.6140000224113464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8049100041389465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5069165825843811},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5017337799072266},{"id":"https://openalex.org/C2778514511","wikidata":"https://www.wikidata.org/wiki/Q1374194","display_name":"Programmer","level":2,"score":0.41895946860313416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36806005239486694},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3569968342781067},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.29688316583633423},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2995628494","doi":null,"is_oa":false,"landing_page_url":"https://openreview.net/pdf?id=ryxjnREFwH","pdf_url":null,"source":{"id":"https://openalex.org/S4306419637","display_name":"International Conference on Learning Representations","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Learning Representations","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.8799999952316284,"display_name":"Quality education","id":"https://metadata.un.org/sdg/4"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1968245656","https://openalex.org/W2064675550","https://openalex.org/W2101964891","https://openalex.org/W2133564696","https://openalex.org/W2230472587","https://openalex.org/W2546950329","https://openalex.org/W2551396370","https://openalex.org/W2552027021","https://openalex.org/W2607964821","https://openalex.org/W2613404084","https://openalex.org/W2626778328","https://openalex.org/W2740747242","https://openalex.org/W2751448157","https://openalex.org/W2757361303","https://openalex.org/W2785423445","https://openalex.org/W2805430026","https://openalex.org/W2888296173","https://openalex.org/W2891991579","https://openalex.org/W2908242605","https://openalex.org/W2908791737","https://openalex.org/W2915816387","https://openalex.org/W2919420119","https://openalex.org/W2944982877","https://openalex.org/W2949800357","https://openalex.org/W2950813464","https://openalex.org/W2951939640","https://openalex.org/W2962800603","https://openalex.org/W2963310665","https://openalex.org/W2963341956","https://openalex.org/W2963564796","https://openalex.org/W2963655793","https://openalex.org/W2963748441","https://openalex.org/W2963794306","https://openalex.org/W2964224049","https://openalex.org/W2965373594","https://openalex.org/W2968677099","https://openalex.org/W2971497145","https://openalex.org/W2973123473"],"related_works":["https://openalex.org/W4226059544","https://openalex.org/W2971497145","https://openalex.org/W4388914936","https://openalex.org/W4306676762","https://openalex.org/W7131266425","https://openalex.org/W2970900584","https://openalex.org/W6912020609","https://openalex.org/W2951365061","https://openalex.org/W2968677099","https://openalex.org/W2553921258"],"abstract_inverted_index":{"Integrating":[0],"distributed":[1],"representations":[2,116],"with":[3,129,141],"symbolic":[4],"operations":[5],"is":[6,67,78],"essential":[7],"for":[8,92,117],"reading":[9,147],"comprehension":[10,148],"requiring":[11],"complex":[12,33,100,119],"reasoning,":[13,153,185],"such":[14],"as":[15],"counting,":[16],"sorting":[17],"and":[18,55,57,114,137],"arithmetics,":[19],"but":[20],"most":[21],"existing":[22],"approaches":[23],"are":[24,214],"hard":[25,138],"to":[26,28,51,62,69,74,122],"scale":[27],"more":[29,32,79,118],"domains":[30],"or":[31],"reasoning.":[34,120],"In":[35],"this":[36],"work,":[37],"we":[38,132],"propose":[39],"the":[40,53,71,87,108,124,160,166,172,196,204,211],"Neural":[41],"Symbolic":[42],"Reader":[43],"(NeRd),":[44],"which":[45,111],"includes":[46],"a":[47,58,64,145,176],"reader,":[48],"e.g.,":[49,60],"BERT,":[50],"encode":[52],"passage":[54],"question,":[56],"programmer,":[59],"LSTM,":[61],"generate":[63],"program":[65,212],"that":[66,150,180],"executed":[68],"produce":[70],"answer.":[72],"Compared":[73],"previous":[75],"works,":[76],"NeRd":[77,128,154,169,201],"scalable":[80],"in":[81],"two":[82],"aspects:":[83],"(1)":[84],"domain-agnostic,":[85],"i.e.,":[86,97],"same":[88,167],"neural":[89],"architecture":[90],"works":[91],"different":[93],"domains;":[94],"(2)":[95],"compositional,":[96],"when":[98,192,207],"needed,":[99],"programs":[101],"can":[102],"be":[103],"generated":[104],"by":[105,186],"recursively":[106],"applying":[107],"predefined":[109],"operators,":[110],"become":[112],"executable":[113],"interpretable":[115],"Furthermore,":[121],"overcome":[123],"challenge":[125],"of":[126,184,210],"training":[127],"weak":[130],"supervision,":[131],"apply":[133],"data":[134],"augmentation":[135],"techniques":[136],"Expectation-Maximization":[139],"(EM)":[140],"thresholding.":[142],"On":[143],"DROP,":[144],"challenging":[146],"dataset":[149],"requires":[151,181],"discrete":[152],"achieves":[155],"2.5%/1.8%":[156],"absolute":[157,188],"improvement":[158],"over":[159],"state-of-the-art":[161],"on":[162,174,190,194],"EM/F1":[163],"metrics.":[164],"With":[165],"architecture,":[168],"significantly":[170],"outperforms":[171],"baselines":[173,205],"MathQA,":[175],"math":[177],"problem":[178],"benchmark":[179],"multiple":[182],"steps":[183],"25.5%":[187],"increment":[189],"accuracy":[191],"trained":[193],"all":[195],"annotated":[197],"programs.":[198],"More":[199],"importantly,":[200],"still":[202],"beats":[203],"even":[206],"only":[208],"20%":[209],"annotations":[213],"given.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":9}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-12-26T00:00:00"}