{"id":"https://openalex.org/W7164735498","doi":"https://doi.org/10.5715/jnlp.33.570","title":"Agreement-Constrained Efficient Probabilistic Minimum Bayes Risk Decoding with Knowledge Distillation Metrics","display_name":"Agreement-Constrained Efficient Probabilistic Minimum Bayes Risk Decoding with Knowledge Distillation Metrics","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7164735498","doi":"https://doi.org/10.5715/jnlp.33.570"},"language":"en","primary_location":{"id":"doi:10.5715/jnlp.33.570","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.33.570","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/33/2/33_570/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/jnlp/33/2/33_570/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114847292","display_name":"Koki Natsumi","orcid":null},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koki Natsumi","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138607017","display_name":"Deguchi Hiroyuki","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092597","display_name":"NTT (United States)","ror":"https://ror.org/004cn7092","country_code":"US","type":"company","lineage":["https://openalex.org/I2251713219","https://openalex.org/I4210092597"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deguchi Hiroyuki","raw_affiliation_strings":["NTT, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT, Inc","institution_ids":["https://openalex.org/I4210092597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042174785","display_name":"Yusuke Sakai","orcid":"https://orcid.org/0000-0001-8810-4813"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yusuke Sakai","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016936747","display_name":"Hidetaka Kamigaito","orcid":"https://orcid.org/0000-0002-5249-5813"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hidetaka Kamigaito","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138598013","display_name":"Taro Watanabe","orcid":null},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taro Watanabe","raw_affiliation_strings":["Nara Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology","institution_ids":["https://openalex.org/I75917431"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61722823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"2","first_page":"570","last_page":"590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.48030000925064087,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.48030000925064087,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.09849999845027924,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.033900000154972076,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.6230999827384949},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5164999961853027},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4514000117778778},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.33899998664855957},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2946999967098236},{"id":"https://openalex.org/keywords/knowledge-based-systems","display_name":"Knowledge-based systems","score":0.28519999980926514},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.28290000557899475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388000249862671},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6230999827384949},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48179998993873596},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4514000117778778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4226999878883362},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27230000495910645},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.25369998812675476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2533000111579895},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5715/jnlp.33.570","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.33.570","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/33/2/33_570/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5715/jnlp.33.570","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.33.570","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/33/2/33_570/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.46856799721717834}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7164735498.pdf","grobid_xml":"https://content.openalex.org/works/W7164735498.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"\u6700\u5c0f\u30d9\u30a4\u30ba\u30ea\u30b9\u30af":[0],"(Minimum":[1],"Bayes":[2],"Risk;":[3],"MBR)":[4],"\u5fa9\u53f7\u306f\uff0c\u51fa\u529b\u5019\u88dc\u306e\u671f\u5f85\u52b9\u7528\u3092\u6700\u5927\u5316\u3059\u308b\u3053\u3068\u3067\u9ad8\u54c1\u8cea\u306a\u7ffb\u8a33\u3092\u751f\u6210\u3059\u308b\u624b\u6cd5\u3067\u3042\u308b\u304c\uff0c\u5019\u88dc\u96c6\u5408\u5185\u306e\u5168\u7d44\u307f\u5408\u308f\u305b\u306b\u5bfe\u3057\u3066\u30b9\u30b3\u30a2\uff08\u52b9\u7528\uff09\u3092\u7b97\u51fa\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\uff0c\u5019\u88dc\u6570\u306b\u5bfe\u3057\u3066\u4e8c\u4e57\u306e\u8a08\u7b97\u6642\u9593\u3092\u8981\u3059\u308b\uff0e\u3053\u306e\u8a08\u7b97\u30b3\u30b9\u30c8\u3092\u524a\u6e1b\u3059\u308b\u305f\u3081\uff0c\u78ba\u7387\u7684\u6700\u5c0f\u30d9\u30a4\u30ba\u30ea\u30b9\u30af":[5],"(Probabilistic":[6],"MBR;":[7],"PMBR)":[8],"\u5fa9\u53f7\u3067\u306f\uff0c\u5019\u88dc\u6587\u306e\u4e00\u90e8\u30da\u30a2\u306b\u5bfe\u3057\u3066\u306e\u307f\u8a55\u4fa1\u6307\u6a19\u306b\u3088\u308b\u30b9\u30b3\u30a2\u3092\u8a08\u7b97\u3057\uff0c\u6b8b\u308a\u306e\u6b20\u640d\u30b9\u30b3\u30a2\u306f\u884c\u5217\u88dc\u5b8c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u3063\u3066\u88dc\u5b8c\u3059\u308b\uff0e\u3057\u304b\u3057\uff0cPMBR\u5fa9\u53f7\u306b\u306f\u3053\u306e\u3088\u3046\u306b\u8a55\u4fa1\u6307\u6a19\u306e\u547c\u3073\u51fa\u3057\u56de\u6570\u3092\u6e1b\u3089\u3059\u3068\uff0c\u7ffb\u8a33\u54c1\u8cea\u304c\u4f4e\u4e0b\u3057\u3066\u3057\u307e\u3046\u554f\u984c\u304c\u3042\u308b\uff0e\u305d\u3053\u3067\u672c\u7814\u7a76\u3067\u306f\uff0c\u77e5\u8b58\u84b8\u7559\u30e2\u30c7\u30eb\u3092\u7528\u3044\u3066\u30b9\u30b3\u30a2\u884c\u5217\u306e\u88dc\u5b8c\u3092\u8a98\u5c0e\u3059\u308b\u5408\u610f\u5236\u7d04\u4ed8\u304dPMBR":[9],"(Agreement-constrained":[10],"PMBR;":[11],"AC-PMBR)":[12],"\u5fa9\u53f7\u3092\u63d0\u6848\u3057\uff0c\u54c1\u8cea\u3068\u8a08\u7b97\u30b3\u30b9\u30c8\u306e\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u3092\u6539\u5584\u3059\u308b\uff0e\u63d0\u6848\u624b\u6cd5\u3067\u3042\u308bAC-PMBR\u5fa9\u53f7\u306f\uff0c\u884c\u5217\u88dc\u5b8c\u306b\u304a\u3051\u308b\u8fd1\u4f3c\u8aa4\u5dee\u3092\u6700\u59273\u500d\u6539\u5584\u3057\uff0cWMT\u201923\u82f1\u2194\u72ec\u7ffb\u8a33\u30bf\u30b9\u30af\u306b\u304a\u3044\u3066\uff0cPMBR\u5fa9\u53f7\u3068\u540c\u7a0b\u5ea6\u306e\u8a08\u7b97\u30b3\u30b9\u30c8\u3067\u3088\u308a\u9ad8\u3044\u7ffb\u8a33\u54c1\u8cea\u3092\u9054\u6210\u3057\u305f\uff0e":[13]},"counts_by_year":[],"updated_date":"2026-08-23T07:36:19.812096","created_date":"2026-06-15T00:00:00"}
