{"id":"https://openalex.org/W2798664666","doi":"https://doi.org/10.18653/v1/p18-1037","title":"AMR Parsing as Graph Prediction with Latent Alignment","display_name":"AMR Parsing as Graph Prediction with Latent Alignment","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2798664666","doi":"https://doi.org/10.18653/v1/p18-1037","mag":"2798664666"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-1037","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1037","pdf_url":"https://www.aclweb.org/anthology/P18-1037.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-1037.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051361948","display_name":"Chunchuan Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chunchuan Lyu","raw_affiliation_strings":["ILCC, School of Informatics, University of Edinburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ILCC, School of Informatics, University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086717154","display_name":"Ivan Titov","orcid":"https://orcid.org/0000-0002-2583-1893"},"institutions":[{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB","NL"],"is_corresponding":false,"raw_author_name":"Ivan Titov","raw_affiliation_strings":["ILCC, School of Informatics, University of Edinburgh","ILLC, University of Amsterdam","University of Amsterdam, Amsterdam, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ILCC, School of Informatics, University of Edinburgh","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"ILLC, University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5659,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70248369,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"397","last_page":"407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.815606951713562},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7944800853729248},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7105618119239807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6389375925064087},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6104525327682495},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6004707217216492},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5441629886627197},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5408663153648376},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5325295925140381},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.479250431060791},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4655360281467438},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4153895080089569},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.301098495721817},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.18155622482299805}],"concepts":[{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.815606951713562},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7944800853729248},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7105618119239807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6389375925064087},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6104525327682495},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6004707217216492},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5441629886627197},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5408663153648376},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5325295925140381},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.479250431060791},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4655360281467438},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4153895080089569},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.301098495721817},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.18155622482299805},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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":8,"locations":[{"id":"doi:10.18653/v1/p18-1037","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1037","pdf_url":"https://www.aclweb.org/anthology/P18-1037.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:publications/76d7536f-3187-4236-9f70-1b0db02ccd2a","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/amr-parsing-as-graph-prediction-with-latent-alignment(76d7536f-3187-4236-9f70-1b0db02ccd2a).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lyu, C & Titov, I 2018, AMR parsing as graph prediction with latent alignment. in I Gurevych & Y Miyao (eds), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference : July 15-20, 2018, Melbourne, Australia. vol. 1, Stroudsburg, PA, pp. 397-407, 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15/07/18. https://doi.org/10.18653/v1/p18-1037","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:arXiv.org:1805.05286","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1805.05286","pdf_url":"https://arxiv.org/pdf/1805.05286","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"","raw_type":null},{"id":"mag:2798664666","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1805.05286.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/76d7536f-3187-4236-9f70-1b0db02ccd2a","is_oa":true,"landing_page_url":"https://hdl.handle.net/11245.1/76d7536f-3187-4236-9f70-1b0db02ccd2a","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lyu, C & Titov, I 2018, AMR parsing as graph prediction with latent alignment. in I Gurevych & Y Miyao (eds), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference : July 15-20, 2018, Melbourne, Australia. vol. 1, Stroudsburg, PA, pp. 397-407, 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15/07/18. https://doi.org/10.18653/v1/p18-1037","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/c1217338-dd1a-4db5-9821-0398262355d3","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11820/c1217338-dd1a-4db5-9821-0398262355d3","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lyu, C & Titov, I 2018, AMR Parsing as Graph Prediction with Latent Alignment. in Proceedings of the 56th Annual Conference of the Association for Computational Linguistics (ACL). Melbourne, Australia , pp. 397-407, 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Victoria, Australia, 15/07/18. < http://aclweb.org/anthology/P18-1037 >","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.ed.ac.uk:publications/c1217338-dd1a-4db5-9821-0398262355d3","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/c1217338-dd1a-4db5-9821-0398262355d3","pdf_url":null,"source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lyu, C & Titov, I 2018, AMR Parsing as Graph Prediction with Latent Alignment. in Proceedings of the 56th Annual Conference of the Association for Computational Linguistics (ACL). Melbourne, Australia , pp. 397-407, 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Victoria, Australia, 15/07/18. < http://aclweb.org/anthology/P18-1037 >","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.1805.05286","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1805.05286","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-1037","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1037","pdf_url":"https://www.aclweb.org/anthology/P18-1037.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G5226226009","display_name":"Scaling Semantic Parsing to Unrestricted Domains","funder_award_id":"639.022.518","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2798664666.pdf","grobid_xml":"https://content.openalex.org/works/W2798664666.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W655477013","https://openalex.org/W1522301498","https://openalex.org/W1829822087","https://openalex.org/W2006969979","https://openalex.org/W2039133703","https://openalex.org/W2103575000","https://openalex.org/W2124204950","https://openalex.org/W2149837184","https://openalex.org/W2224454470","https://openalex.org/W2250623140","https://openalex.org/W2251071803","https://openalex.org/W2251599843","https://openalex.org/W2252123671","https://openalex.org/W2307268612","https://openalex.org/W2460641690","https://openalex.org/W2547875792","https://openalex.org/W2550147980","https://openalex.org/W2563976356","https://openalex.org/W2600702321","https://openalex.org/W2619249607","https://openalex.org/W2621537751","https://openalex.org/W2740241688","https://openalex.org/W2752593721","https://openalex.org/W2758846169","https://openalex.org/W2899771611","https://openalex.org/W2952264928","https://openalex.org/W2963435215","https://openalex.org/W2963828071","https://openalex.org/W2964164798","https://openalex.org/W2964253663","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2962947230","https://openalex.org/W2608053514","https://openalex.org/W2799190973","https://openalex.org/W2251173392","https://openalex.org/W2998054581","https://openalex.org/W3173677700","https://openalex.org/W2528417318","https://openalex.org/W2898852996","https://openalex.org/W3155549525","https://openalex.org/W2161575515","https://openalex.org/W2012233761","https://openalex.org/W3213740841","https://openalex.org/W192751779","https://openalex.org/W1963721578","https://openalex.org/W3207813505","https://openalex.org/W2301095666","https://openalex.org/W3175168675","https://openalex.org/W1969245183","https://openalex.org/W1754336521","https://openalex.org/W2585527517"],"abstract_inverted_index":{"meaning":[0],"representations":[1],"(AMRs)":[2],"are":[3],"broad-coverage":[4],"sentence-level":[5],"semantic":[6],"representations.":[7],"AMRs":[8],"represent":[9],"sentences":[10],"as":[11,48],"rooted":[12],"labeled":[13],"directed":[14],"acyclic":[15],"graphs.":[16],"AMR":[17],"parsing":[18],"is":[19,69,90],"challenging":[20],"partly":[21],"due":[22],"to":[23,92],"the":[24,32,37,73,82,103,108],"lack":[25],"of":[26,56,81,96],"annotated":[27],"alignments":[28,47,67],"between":[29],"nodes":[30],"in":[31,36],"graphs":[33],"and":[34,59,68,77,98],"words":[35],"corresponding":[38],"sentences.":[39],"We":[40,85],"introduce":[41],"a":[42,52,78,94],"neural":[43],"parser":[44,101],"which":[45],"treats":[46],"latent":[49],"variables":[50],"within":[51],"joint":[53,88],"probabilistic":[54],"model":[55],"concepts,":[57],"relations":[58],"alignments.":[60,84],"As":[61],"exact":[62],"inference":[63],"requires":[64],"marginalizing":[65],"over":[66],"infeasible,":[70],"we":[71],"use":[72],"variational":[74],"autoencoding":[75],"framework":[76],"continuous":[79],"relaxation":[80],"discrete":[83],"show":[86],"that":[87],"modeling":[89],"preferable":[91],"using":[93],"pipeline":[95],"align":[97],"parse.":[99],"The":[100],"achieves":[102],"best":[104],"reported":[105],"results":[106],"on":[107,112],"standard":[109],"benchmark":[110],"(74.4%":[111],"LDC2016E25).":[113]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":4}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}