{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PUVVH3K4P3MQ3ENMDVMZDILLY2","short_pith_number":"pith:PUVVH3K4","schema_version":"1.0","canonical_sha256":"7d2b53ed5c7ed90d91ac1d5991a16bc693fa5b91b4045acce3b62897431d7cdd","source":{"kind":"arxiv","id":"1904.06618","version":1},"attestation_state":"computed","paper":{"title":"UR-FUNNY: A Multimodal Language Dataset for Understanding Humor","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amir Zadeh, Jianyuan Zhong, Louis-Philippe Morency, Md Iftekhar Tanveer, Md Kamrul Hasan, Mohammed (Ehsan) Hoque, Wasifur Rahman","submitted_at":"2019-04-14T03:15:38Z","abstract_excerpt":"Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.06618","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-04-14T03:15:38Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"2e02438ae0add172d7bc35747892a6e30f1958eacc17f5dcf73b3a0e232f5543","abstract_canon_sha256":"6bbe4b09066ac77adbefef6987ebf1d70ee15c0acf1a09ceb0aca82b595b22c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:15:21.784135Z","signature_b64":"jWJyPTCbDEVhAPbNMO+tl8L8gHxwWZUwqwY9M43MU6BvH3K6ytbvr9/hSZ5n3FY+nmhacqU0neLxxfx4BrKVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d2b53ed5c7ed90d91ac1d5991a16bc693fa5b91b4045acce3b62897431d7cdd","last_reissued_at":"2026-07-05T01:15:21.783664Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:15:21.783664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UR-FUNNY: A Multimodal Language Dataset for Understanding Humor","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amir Zadeh, Jianyuan Zhong, Louis-Philippe Morency, Md Iftekhar Tanveer, Md Kamrul Hasan, Mohammed (Ehsan) Hoque, Wasifur Rahman","submitted_at":"2019-04-14T03:15:38Z","abstract_excerpt":"Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06618","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1904.06618/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.06618","created_at":"2026-07-05T01:15:21.783723+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.06618v1","created_at":"2026-07-05T01:15:21.783723+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06618","created_at":"2026-07-05T01:15:21.783723+00:00"},{"alias_kind":"pith_short_12","alias_value":"PUVVH3K4P3MQ","created_at":"2026-07-05T01:15:21.783723+00:00"},{"alias_kind":"pith_short_16","alias_value":"PUVVH3K4P3MQ3ENM","created_at":"2026-07-05T01:15:21.783723+00:00"},{"alias_kind":"pith_short_8","alias_value":"PUVVH3K4","created_at":"2026-07-05T01:15:21.783723+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11614","citing_title":"Information-Theoretic Decomposition for Multimodal Interaction Learning","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2511.21331","citing_title":"The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06309","citing_title":"MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06309","citing_title":"MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18460","citing_title":"Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective","ref_index":171,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2","json":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2.json","graph_json":"https://pith.science/api/pith-number/PUVVH3K4P3MQ3ENMDVMZDILLY2/graph.json","events_json":"https://pith.science/api/pith-number/PUVVH3K4P3MQ3ENMDVMZDILLY2/events.json","paper":"https://pith.science/paper/PUVVH3K4"},"agent_actions":{"view_html":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2","download_json":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2.json","view_paper":"https://pith.science/paper/PUVVH3K4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.06618&json=true","fetch_graph":"https://pith.science/api/pith-number/PUVVH3K4P3MQ3ENMDVMZDILLY2/graph.json","fetch_events":"https://pith.science/api/pith-number/PUVVH3K4P3MQ3ENMDVMZDILLY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2/action/storage_attestation","attest_author":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2/action/author_attestation","sign_citation":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2/action/citation_signature","submit_replication":"https://pith.science/pith/PUVVH3K4P3MQ3ENMDVMZDILLY2/action/replication_record"}},"created_at":"2026-07-05T01:15:21.783723+00:00","updated_at":"2026-07-05T01:15:21.783723+00:00"}