{"id":"https://openalex.org/W7161232580","doi":"https://doi.org/10.48550/arxiv.2603.21174","title":"Explainable Semantic Textual Similarity via Dissimilar Span Detection","display_name":"Explainable Semantic Textual Similarity via Dissimilar Span Detection","publication_year":2026,"publication_date":"2026-03-22","ids":{"openalex":"https://openalex.org/W7161232580","doi":"https://doi.org/10.48550/arxiv.2603.21174"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21174","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21174","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.21174","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111015532","display_name":"Diego Miguel Lozano","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lozano, Diego Miguel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136206340","display_name":"Daryna Dementieva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dementieva, Daryna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136191523","display_name":"Alexander Fraser","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fraser, Alexander","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7475000023841858,"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":0.7475000023841858,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.061400000005960464,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.036400001496076584,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/paraphrase","display_name":"Paraphrase","score":0.6561999917030334},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6474999785423279},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.6399999856948853},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6075999736785889},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5985999703407288},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5281000137329102},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.44620001316070557},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.390500009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7534000277519226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6973000168800354},{"id":"https://openalex.org/C2780922921","wikidata":"https://www.wikidata.org/wiki/Q255189","display_name":"Paraphrase","level":2,"score":0.6561999917030334},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6488000154495239},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6474999785423279},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.6399999856948853},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6075999736785889},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5985999703407288},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4397999942302704},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C2778753569","wikidata":"https://www.wikidata.org/wiki/Q1960395","display_name":"Span (engineering)","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.250900000333786},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21174","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21174","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:10.48550/arxiv.2603.21174","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21174","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5205556750297546,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"Textual":[1],"Similarity":[2,88],"(STS)":[3],"is":[4],"a":[5,23,79,93],"crucial":[6],"component":[7],"of":[8,35,49,147,170],"many":[9],"Natural":[10],"Language":[11],"Processing":[12],"(NLP)":[13],"applications.":[14],"However,":[15],"existing":[16],"approaches":[17],"typically":[18],"reduce":[19],"semantic":[20],"nuances":[21],"to":[22,42,69,163],"single":[24],"score,":[25,65],"limiting":[26],"interpretability.":[27],"To":[28],"address":[29],"this,":[30],"we":[31,77,151],"introduce":[32],"the":[33,63,84,86,137,145,148,167],"task":[34,169],"Dissimilar":[36],"Span":[37,87],"Detection":[38],"(DSD),":[39],"which":[40,56],"aims":[41],"identify":[43],"semantically":[44],"differing":[45],"spans":[46],"between":[47],"pairs":[48],"texts.":[50],"This":[51],"can":[52,161],"help":[53],"users":[54],"understand":[55],"particular":[57],"words":[58],"or":[59,66],"tokens":[60],"negatively":[61],"affect":[62],"similarity":[64],"be":[67],"used":[68],"improve":[70],"performance":[71,165],"in":[72,166],"STS-dependent":[73],"downstream":[74],"tasks.":[75],"Furthermore,":[76],"release":[78],"new":[80],"dataset":[81],"suitable":[82],"for":[83,111],"task,":[85],"Dataset":[89],"(SSD),":[90],"developed":[91],"through":[92],"semi-automated":[94],"pipeline":[95],"combining":[96],"large":[97],"language":[98],"models":[99,135],"(LLMs)":[100],"with":[101],"human":[102],"verification.":[103],"We":[104],"propose":[105],"and":[106,120,133],"evaluate":[107],"different":[108],"baseline":[109],"methods":[110],"DSD,":[112],"both":[113],"unsupervised,":[114],"based":[115],"on":[116],"LIME,":[117],"SHAP,":[118],"LLMs,":[119],"our":[121],"own":[122],"method,":[123],"as":[124,126],"well":[125],"an":[127,154],"additional":[128,155],"supervised":[129,134],"approach.":[130],"While":[131],"LLMs":[132],"achieve":[136],"highest":[138],"performance,":[139],"overall":[140],"results":[141],"remain":[142],"low,":[143],"highlighting":[144],"complexity":[146],"task.":[149],"Finally,":[150],"set":[152],"up":[153],"experiment":[156],"that":[157],"shows":[158],"how":[159],"DSD":[160],"lead":[162],"increased":[164],"specific":[168],"paraphrase":[171],"detection.":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}