{"id":"https://openalex.org/W4404782698","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.1052","title":"Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering","display_name":"Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404782698","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.1052"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2024.emnlp-main.1052","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2024.emnlp-main.1052","pdf_url":"https://aclanthology.org/2024.emnlp-main.1052.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.emnlp-main.1052.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005391755","display_name":"Weihe Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Weihe Zhai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071220716","display_name":"Arkaitz Zubiaga","orcid":"https://orcid.org/0000-0003-4583-3623"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arkaitz Zubiaga","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004324294","display_name":"Bingquan Liu","orcid":"https://orcid.org/0000-0001-6352-2170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bingquan Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054077705","display_name":"Chengjie Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengjie Sun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101926384","display_name":"Yalong Zhao","orcid":"https://orcid.org/0000-0002-1942-057X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yalong Zhao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5005391755"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19565104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"18920","last_page":"18930"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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":0.9983999729156494,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9922000169754028,"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/T11719","display_name":"Data Quality and Management","score":0.9642000198364258,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.8452390432357788},{"id":"https://openalex.org/keywords/commonsense-knowledge","display_name":"Commonsense knowledge","score":0.7835569381713867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6992674469947815},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5214169025421143},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5077139139175415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5050650238990784},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4899391531944275},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46573013067245483},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.4378800690174103},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.19497200846672058},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16112598776817322}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8452390432357788},{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.7835569381713867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992674469947815},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5214169025421143},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5077139139175415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5050650238990784},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4899391531944275},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46573013067245483},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.4378800690174103},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.19497200846672058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16112598776817322}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.emnlp-main.1052","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2024.emnlp-main.1052","pdf_url":"https://aclanthology.org/2024.emnlp-main.1052.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.emnlp-main.1052","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/2024.emnlp-main.1052","pdf_url":"https://aclanthology.org/2024.emnlp-main.1052.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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404782698.pdf","grobid_xml":"https://content.openalex.org/works/W4404782698.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3035583586","https://openalex.org/W4320165839","https://openalex.org/W2151799802","https://openalex.org/W2196562041","https://openalex.org/W4385488510","https://openalex.org/W2073302931","https://openalex.org/W3206107299","https://openalex.org/W3082691151","https://openalex.org/W4287633646","https://openalex.org/W4313191056"],"abstract_inverted_index":{"The":[0],"fusion":[1],"of":[2],"language":[3],"models":[4],"(LMs)":[5],"and":[6,36,42,64,90,99],"knowledge":[7],"graphs":[8],"(KGs)":[9],"is":[10],"widely":[11],"used":[12],"in":[13],"commonsense":[14,111],"question":[15],"answering,":[16],"but":[17],"generating":[18],"faithful":[19],"explanations":[20,81],"remains":[21],"challenging.Current":[22],"methods":[23],"often":[24],"overlook":[25],"path":[26],"decoding":[27],"faithfulness,":[28],"leading":[29],"to":[30,59,72,105],"divergence":[31],"between":[32],"graph":[33],"encoder":[34],"outputs":[35],"model":[37,100],"predictions.We":[38],"identify":[39],"confounding":[40],"effects":[41],"LM-KG":[43,56,67],"misalignment":[44,108],"as":[45],"key":[46],"factors":[47],"causing":[48],"spurious":[49],"explanations.To":[50],"address":[51,106],"this,":[52],"we":[53,78],"introduce":[54],"the":[55,66,83,103],"Fidelity":[57],"metric":[58],"assess":[60],"KG":[61,80],"representation":[62],"reliability":[63],"propose":[65],"Distribution-aware":[68],"Alignment":[69],"(LKDA)":[70],"algorithm":[71],"improve":[73],"explanation":[74,97],"faithfulness.Without":[75],"ground":[76],"truth,":[77],"evaluate":[79],"using":[82],"proposed":[84],"Fidelity-Sparsity":[85],"Trade-off":[86],"Curve.Experiments":[87],"on":[88],"Common-senseQA":[89],"OpenBookQA":[91],"show":[92],"that":[93],"LKDA":[94],"significantly":[95],"enhances":[96],"fidelity":[98],"performance,":[101],"highlighting":[102],"need":[104],"distributional":[107],"for":[109],"reliable":[110],"reasoning.":[112]},"counts_by_year":[],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}