{"id":"https://openalex.org/W7166883852","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1790","title":"EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric","display_name":"EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166883852","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1790"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1790","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1790","pdf_url":"https://aclanthology.org/2026.findings-acl.1790.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.1790.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139806055","display_name":"Dongning Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongning Rao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139752822","display_name":"Zhihua Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhihua Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139718720","display_name":"Zhihua Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhihua Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.89317245,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"35921","last_page":"35942"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.6449999809265137,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.6449999809265137,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.1251000016927719,"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":0.04010000079870224,"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/metric","display_name":"Metric (unit)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/empathy","display_name":"Empathy","score":0.48510000109672546},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29789999127388},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2558000087738037}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5827000141143799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.569100022315979},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C2779885105","wikidata":"https://www.wikidata.org/wiki/Q182263","display_name":"Empathy","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3504999876022339},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3041999936103821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27900001406669617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1790","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1790","pdf_url":"https://aclanthology.org/2026.findings-acl.1790.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.1790","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1790","pdf_url":"https://aclanthology.org/2026.findings-acl.1790.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.42154499888420105}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166883852.pdf","grobid_xml":"https://content.openalex.org/works/W7166883852.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Empathy":[0],"is":[1,174],"key":[2],"to":[3,14,24,91,128],"many":[4],"professions.In":[5],"recognition":[6],"of":[7,171],"this,":[8],"the":[9,34,55,65,87,96,122,131,139,149,153,164,169],"workshops":[10],"on":[11,130,152],"computational":[12],"approaches":[13],"subjectivity,":[15],"sentiment,":[16],"and":[17,79,113,156],"social":[18],"media":[19],"analysis":[20],"(WASSA)":[21],"hosted":[22],"competitions":[23],"evaluate":[25],"empathy":[26,45],"in":[27,33],"dialogue.While":[28],"fine-tuning":[29],"has":[30],"proved":[31],"successful":[32],"competition,":[35],"there":[36],"are":[37,46],"at":[38],"least":[39],"three":[40],"shortcomings.First,":[41],"novel":[42,81,83],"metrics":[43,51],"for":[44,168],"absent.Second,":[47],"classical":[48,75],"dialogue":[49,76],"evaluation":[50,77],"require":[52],"further":[53],"investigation.Third,":[54],"ensemble's":[56],"potential":[57],"remained":[58],"underdeveloped.To":[59],"address":[60],"these":[61],"issues,":[62],"we":[63,108,134],"propose":[64],"EMPATH":[66],"framework,":[67],"which":[68,118],"combines":[69],"fine-tuned":[70],"models,":[71,74],"large":[72],"language":[73],"metrics,":[78],"a":[80,100,105,110],"metric.The":[82],"metric,":[84],"ED,":[85],"encourages":[86],"response's":[88],"emotional":[89],"tone":[90],"be":[92],"contextually":[93],"appropriate.E.g.,":[94],"if":[95],"user":[97],"expresses":[98],"joy,":[99],"cheerful":[101],"reaction":[102],"should":[103],"receive":[104],"higher":[106],"ranking.Furthermore,":[107],"introduce":[109],"new":[111],"robust":[112],"label-free":[114],"ensemble":[115],"strategy,":[116],"HO,":[117],"integrates":[119],"submetrics":[120],"with":[121],"lowest":[123],"correlation":[124,166],"coefficient":[125,167],"first.In":[126],"addition":[127],"evaluating":[129],"WASSA":[132,172],"benchmark,":[133],"test":[135],"EMPATH's":[136],"generalizability":[137],"using":[138],"EmpatheticExchanges":[140],"dataset":[141],"(EX).Our":[142],"experiment":[143],"results":[144,151],"demonstrate":[145],"that":[146],"EM-PATH":[147],"yields":[148],"best":[150],"competition":[154],"dataset,":[155],"ablation":[157],"studies":[158],"validate":[159],"our":[160],"component":[161],"selection.On":[162],"EX,":[163],"Pearson":[165],"winner":[170],"2024":[173],"0.":[175]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
