{"id":"https://openalex.org/W2758343314","doi":"https://doi.org/10.18653/v1/d17-1084","title":"Learning Fine-Grained Expressions to Solve Math Word Problems","display_name":"Learning Fine-Grained Expressions to Solve Math Word Problems","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2758343314","doi":"https://doi.org/10.18653/v1/d17-1084","mag":"2758343314"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1084","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1084","pdf_url":"https://www.aclweb.org/anthology/D17-1084.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D17-1084.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061321029","display_name":"Danqing Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danqing Huang","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087920747","display_name":"Shuming Shi","orcid":"https://orcid.org/0000-0001-7018-0682"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuming Shi","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090151187","display_name":"Chin-Yew Lin","orcid":"https://orcid.org/0000-0002-0798-6365"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chin-Yew Lin","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017205177","display_name":"Jian Yin","orcid":"https://orcid.org/0000-0002-1214-5384"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yin","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061321029"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":6.2358,"has_fulltext":true,"cited_by_count":95,"citation_normalized_percentile":{"value":0.97066869,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"805","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9919999837875366,"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":0.9919999837875366,"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":0.989799976348877,"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":0.9581000208854675,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8416328430175781},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.7400532960891724},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.68124920129776},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6487765312194824},{"id":"https://openalex.org/keywords/template","display_name":"Template","score":0.6262850761413574},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5787839293479919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44318878650665283},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4190611243247986},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.39160656929016113},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2449914813041687},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09584349393844604}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8416328430175781},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.7400532960891724},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.68124920129776},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6487765312194824},{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.6262850761413574},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5787839293479919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44318878650665283},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4190611243247986},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39160656929016113},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2449914813041687},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09584349393844604},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d17-1084","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1084","pdf_url":"https://www.aclweb.org/anthology/D17-1084.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d17-1084","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1084","pdf_url":"https://www.aclweb.org/anthology/D17-1084.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G1941139581","display_name":null,"funder_award_id":"U1611264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4044104431","display_name":null,"funder_award_id":"U1401256","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4420049336","display_name":null,"funder_award_id":"U1401256, U1501252","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5229126207","display_name":null,"funder_award_id":"61472453","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8698608195","display_name":null,"funder_award_id":"U1501252","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2758343314.pdf","grobid_xml":"https://content.openalex.org/works/W2758343314.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W25883864","https://openalex.org/W46758386","https://openalex.org/W1505640990","https://openalex.org/W1506904803","https://openalex.org/W2003505241","https://openalex.org/W2013938288","https://openalex.org/W2021901949","https://openalex.org/W2072950331","https://openalex.org/W2092771413","https://openalex.org/W2098209811","https://openalex.org/W2105717194","https://openalex.org/W2250564385","https://openalex.org/W2250769864","https://openalex.org/W2251349042","https://openalex.org/W2251935656","https://openalex.org/W2252202991","https://openalex.org/W2276364082","https://openalex.org/W2510828927","https://openalex.org/W2513499049","https://openalex.org/W2561975193","https://openalex.org/W2988119488"],"related_works":["https://openalex.org/W2378994405","https://openalex.org/W2385974820","https://openalex.org/W2373478030","https://openalex.org/W2378679551","https://openalex.org/W3149739944","https://openalex.org/W2392363776","https://openalex.org/W2063051341","https://openalex.org/W2591066345","https://openalex.org/W1494563618","https://openalex.org/W2357022711"],"abstract_inverted_index":{"This":[0,12],"paper":[1],"presents":[2],"a":[3,39,58,64,87],"novel":[4],"templatebased":[5],"method":[6,13,100],"to":[7,77,90],"solve":[8],"math":[9,18,22,27,74],"word":[10,23,75],"problems.":[11],"learns":[14],"the":[15,50,92,107,131],"mappings":[16],"between":[17],"concept":[19],"phrases":[20],"in":[21,57,73],"problems":[24,48,76],"and":[25,70],"their":[26],"expressions":[28],"from":[29,46],"training":[30],"data.":[31],"For":[32],"each":[33],"equation":[34,67,82],"template,":[35],"we":[36],"automatically":[37],"construct":[38],"rich":[40],"template":[41],"sketch":[42],"by":[43],"aggregating":[44],"information":[45],"various":[47],"with":[49],"same":[51],"template.":[52],"Our":[53],"approach":[54],"is":[55,112],"implemented":[56],"two-stage":[59],"system.":[60],"It":[61,84],"first":[62],"retrieves":[63],"few":[65],"relevant":[66],"system":[68],"templates":[69,79],"aligns":[71],"numbers":[72],"those":[78],"for":[80],"candidate":[81],"generation.":[83],"then":[85],"does":[86],"fine-grained":[88],"inference":[89],"obtain":[91],"final":[93],"answer.":[94],"Experiment":[95],"results":[96],"show":[97],"that":[98],"our":[99],"achieves":[101],"an":[102,123],"accuracy":[103,124],"of":[104,126],"28.4%":[105],"on":[106,130],"linear":[108],"Dolphin18K":[109],"benchmark,":[110],"which":[111],"10%":[113],"(54%":[114],"relative)":[115,129],"higher":[116],"than":[117],"previous":[118],"stateof-the-art":[119],"systems":[120],"while":[121],"achieving":[122],"increase":[125],"12%":[127],"(59%":[128],"TS6":[132],"benchmark":[133],"subset.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":12}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}