{"id":"https://openalex.org/W7140144123","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.239","title":"Linguistic Cues for LLM-based Implicit Discourse Relation Classification","display_name":"Linguistic Cues for LLM-based Implicit Discourse Relation Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140144123","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.239"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-eacl.239","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.239","pdf_url":"https://aclanthology.org/2026.findings-eacl.239.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: EACL 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-eacl.239.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130362733","display_name":"Yi Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I71030271","display_name":"Heidelberg Institute for Theoretical Studies","ror":"https://ror.org/01f7bcy98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I71030271"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yi Fan","raw_affiliation_strings":["Heidelberg Institute for Theoretical Studies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heidelberg Institute for Theoretical Studies","institution_ids":["https://openalex.org/I71030271"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020347604","display_name":"Michael Strube","orcid":"https://orcid.org/0000-0002-4731-8142"},"institutions":[{"id":"https://openalex.org/I71030271","display_name":"Heidelberg Institute for Theoretical Studies","ror":"https://ror.org/01f7bcy98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I71030271"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Strube","raw_affiliation_strings":["Heidelberg Institute for Theoretical Studies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heidelberg Institute for Theoretical Studies","institution_ids":["https://openalex.org/I71030271"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130349031","display_name":"Wei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I71030271","display_name":"Heidelberg Institute for Theoretical Studies","ror":"https://ror.org/01f7bcy98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I71030271"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Heidelberg Institute for Theoretical Studies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heidelberg Institute for Theoretical Studies","institution_ids":["https://openalex.org/I71030271"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I71030271"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4585","last_page":"4602"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.3010999858379364,"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":0.3010999858379364,"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":0.14830000698566437,"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/T13629","display_name":"Text Readability and Simplification","score":0.06880000233650208,"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/relation","display_name":"Relation (database)","score":0.599399983882904},{"id":"https://openalex.org/keywords/discourse-analysis","display_name":"Discourse analysis","score":0.2971999943256378},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.2944999933242798},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.28600001335144043},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.27810001373291016}],"concepts":[{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.6062999963760376},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.46549999713897705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34310001134872437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3052000105381012},{"id":"https://openalex.org/C84389358","wikidata":"https://www.wikidata.org/wiki/Q1129466","display_name":"Discourse analysis","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26080000400543213},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.2596000134944916},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-eacl.239","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.239","pdf_url":"https://aclanthology.org/2026.findings-eacl.239.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: EACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-eacl.239","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.239","pdf_url":"https://aclanthology.org/2026.findings-eacl.239.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: EACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324316","display_name":"Klaus Tschira Stiftung","ror":"https://ror.org/052jep661"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140144123.pdf","grobid_xml":"https://content.openalex.org/works/W7140144123.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1,24],"models":[2,25],"(LLMs)":[3],"have":[4],"achieved":[5],"impressive":[6],"success":[7],"across":[8],"many":[9],"NLP":[10],"tasks,":[11],"yet":[12],"implicit":[13],"discourse":[14],"relation":[15],"classification":[16],"(IDRC)":[17],"is":[18],"still":[19],"dominated":[20],"by":[21,107],"encoder-only":[22],"pretrained":[23],"such":[26],"as":[27],"RoBERTa.This":[28],"may":[29],"be":[30],"due":[31],"to":[32,69,113],"earlier":[33],"reports":[34],"that":[35,48,65,86],"Chat-GPT":[36],"performs":[37],"poorly":[38],"on":[39,53],"IDRC":[40],"in":[41,100],"zero-shot":[42],"settings.In":[43],"this":[44],"paper,":[45],"we":[46,63,81],"show":[47],"fine-tuned":[49],"LLMs":[50,66],"can":[51],"perform":[52],"par":[54],"with,":[55],"or":[56],"even":[57],"better":[58],"than,":[59],"existing":[60],"encoder-based":[61],"approaches.Nevertheless,":[62],"find":[64],"alone":[67],"struggle":[68],"capture":[70],"subtle":[71],"lexical":[72],"relations":[73],"between":[74],"arguments":[75,88],"for":[76],"the":[77],"task.To":[78],"address":[79],"this,":[80],"propose":[82],"a":[83],"two-step":[84],"strategy":[85],"enriches":[87],"with":[89,103],"explicit":[90],"lexical-level":[91],"semantic":[92],"cues":[93],"before":[94],"fine-tuning.Experiments":[95],"demonstrate":[96],"substantial":[97],"gains,":[98],"particularly":[99],"cross-domain":[101],"scenarios,":[102],"F1":[104],"scores":[105],"improved":[106],"more":[108],"than":[109],"10":[110],"points":[111],"compared":[112],"strong":[114],"baselines.":[115]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-24T00:00:00"}
