{"id":"https://openalex.org/W2524520086","doi":"https://doi.org/10.18653/v1/d16-1224","title":"AMR-to-text generation as a Traveling Salesman Problem","display_name":"AMR-to-text generation as a Traveling Salesman Problem","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2524520086","doi":"https://doi.org/10.18653/v1/d16-1224","mag":"2524520086"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d16-1224","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1224","pdf_url":"https://www.aclweb.org/anthology/D16-1224.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 2016 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/D16-1224.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016877422","display_name":"Linfeng Song","orcid":"https://orcid.org/0000-0002-3502-3574"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Linfeng Song","raw_affiliation_strings":["Department of Computer Science, University of Rochester, Rochester, NY 14627"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Rochester, Rochester, NY 14627","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333729","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0002-5214-2268"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100908682","display_name":"Xiaochang Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaochang Peng","raw_affiliation_strings":["Department of Computer Science, University of Rochester, Rochester, NY 14627"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Rochester, Rochester, NY 14627","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430087","display_name":"Zhiguo Wang","orcid":"https://orcid.org/0000-0002-2412-6172"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiguo Wang","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY 10598"],"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY 10598","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020945377","display_name":"Daniel Gildea","orcid":"https://orcid.org/0000-0002-7858-2624"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Gildea","raw_affiliation_strings":["Department of Computer Science, University of Rochester, Rochester, NY 14627"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Rochester, Rochester, NY 14627","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016877422"],"corresponding_institution_ids":["https://openalex.org/I5388228"],"apc_list":null,"apc_paid":null,"fwci":8.3927,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.97612705,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2084","last_page":"2089"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/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/T12031","display_name":"Speech and dialogue systems","score":0.9803000092506409,"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/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.8456004858016968},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6938379406929016},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.603752076625824},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6000356674194336},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.5477142930030823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5308873057365417},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45292675495147705},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.43781575560569763},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42475754022598267},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4044300317764282},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29401522874832153},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.07126444578170776}],"concepts":[{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.8456004858016968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938379406929016},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.603752076625824},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6000356674194336},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.5477142930030823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5308873057365417},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45292675495147705},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.43781575560569763},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42475754022598267},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4044300317764282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29401522874832153},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.07126444578170776},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d16-1224","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1224","pdf_url":"https://www.aclweb.org/anthology/D16-1224.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d16-1224","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d16-1224","pdf_url":"https://www.aclweb.org/anthology/D16-1224.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 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G227858465","display_name":null,"funder_award_id":"201301","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7651818231","display_name":null,"funder_award_id":"61572245","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8016008144","display_name":"EAGER: Collaborative Research: Scaling Up Discriminative Learning for Natural Language Understanding and Translation","funder_award_id":"1446996","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2524520086.pdf","grobid_xml":"https://content.openalex.org/works/W2524520086.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W173713748","https://openalex.org/W201441355","https://openalex.org/W1564002882","https://openalex.org/W1829822087","https://openalex.org/W1908479511","https://openalex.org/W1985610876","https://openalex.org/W1999383775","https://openalex.org/W2080656168","https://openalex.org/W2101105183","https://openalex.org/W2141311720","https://openalex.org/W2149837184","https://openalex.org/W2153653739","https://openalex.org/W2202795099","https://openalex.org/W2250375035","https://openalex.org/W2250623140","https://openalex.org/W2250777616","https://openalex.org/W2250808720","https://openalex.org/W2251823395","https://openalex.org/W2252123671","https://openalex.org/W2296308987","https://openalex.org/W2468355276","https://openalex.org/W4241645538"],"related_works":["https://openalex.org/W1525389557","https://openalex.org/W3116116498","https://openalex.org/W3029012650","https://openalex.org/W2361554335","https://openalex.org/W1988325893","https://openalex.org/W2776212826","https://openalex.org/W2164188042","https://openalex.org/W2359992618","https://openalex.org/W3214323197","https://openalex.org/W4385571001"],"abstract_inverted_index":{"The":[0,77],"task":[1,23],"of":[2,84],"AMR-to-text":[3],"generation":[4],"is":[5,59,70],"to":[6,61,72],"generate":[7],"grammatical":[8],"text":[9],"that":[10],"sustains":[11],"the":[12,22,27,36,44,63,74,87],"semantic":[13],"meaning":[14],"for":[15,38],"a":[16,67,81],"given":[17],"AMR":[18,28],"graph.":[19],"We":[20],"attack":[21],"by":[24,46],"first":[25],"partitioning":[26],"graph":[29],"into":[30],"smaller":[31],"fragments,":[32],"and":[33,66],"then":[34],"generating":[35],"translation":[37],"each":[39],"fragment,":[40],"before":[41],"finally":[42],"deciding":[43],"order":[45],"solving":[47],"an":[48],"asymmetric":[49],"generalized":[50],"traveling":[51,64],"salesman":[52],"problem":[53],"(AGTSP).":[54],"A":[55],"Maximum":[56],"Entropy":[57],"classifier":[58],"trained":[60],"estimate":[62],"costs,":[65],"TSP":[68],"solver":[69],"used":[71],"find":[73],"optimized":[75],"solution.":[76],"final":[78],"model":[79],"reports":[80],"BLEU":[82],"score":[83],"22.44":[85],"on":[86],"SemEval-2016":[88],"Task8":[89],"dataset.":[90]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
