{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:D5ATQZHVS2FX7IM5F5AJPX7ZSI","short_pith_number":"pith:D5ATQZHV","schema_version":"1.0","canonical_sha256":"1f413864f5968b7fa19d2f4097dff9920e4e0999a2bd1738b28c47a755b03688","source":{"kind":"arxiv","id":"1905.05812","version":1},"attestation_state":"computed","paper":{"title":"Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Asif Ekbal, Deepanway Ghosal, Dushyant Singh Chauhan, Md Shad Akhtar, Pushpak Bhattacharyya, Soujanya Poria","submitted_at":"2019-05-14T19:42:43Z","abstract_excerpt":"Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e., text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU"},"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":"1905.05812","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-14T19:42:43Z","cross_cats_sorted":[],"title_canon_sha256":"0f77a80db504ae30ce30ec60f7463d91e90866c08021cab270b19f670410caeb","abstract_canon_sha256":"3acd526ddbe5e4e11b9b773021789e97c7a1fb7531a7b50cd4c514fc5903ef41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:08.463168Z","signature_b64":"BoaoR5zxZlewILlt83LIgJ9/Ebb0Jrn4M3Z97Nxf1lo6JoB8taA23RDnRX6D2sYDp6j1UCfsQSYSG1id7YUYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f413864f5968b7fa19d2f4097dff9920e4e0999a2bd1738b28c47a755b03688","last_reissued_at":"2026-05-17T23:46:08.462724Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:08.462724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Asif Ekbal, Deepanway Ghosal, Dushyant Singh Chauhan, Md Shad Akhtar, Pushpak Bhattacharyya, Soujanya Poria","submitted_at":"2019-05-14T19:42:43Z","abstract_excerpt":"Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e., text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05812","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":""},"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":"1905.05812","created_at":"2026-05-17T23:46:08.462783+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.05812v1","created_at":"2026-05-17T23:46:08.462783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05812","created_at":"2026-05-17T23:46:08.462783+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5ATQZHVS2FX","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5ATQZHVS2FX7IM5","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5ATQZHV","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2409.07388","citing_title":"Recent Advances in Multimodal Affective Computing: An NLP Perspective","ref_index":212,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI","json":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI.json","graph_json":"https://pith.science/api/pith-number/D5ATQZHVS2FX7IM5F5AJPX7ZSI/graph.json","events_json":"https://pith.science/api/pith-number/D5ATQZHVS2FX7IM5F5AJPX7ZSI/events.json","paper":"https://pith.science/paper/D5ATQZHV"},"agent_actions":{"view_html":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI","download_json":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI.json","view_paper":"https://pith.science/paper/D5ATQZHV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.05812&json=true","fetch_graph":"https://pith.science/api/pith-number/D5ATQZHVS2FX7IM5F5AJPX7ZSI/graph.json","fetch_events":"https://pith.science/api/pith-number/D5ATQZHVS2FX7IM5F5AJPX7ZSI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI/action/storage_attestation","attest_author":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI/action/author_attestation","sign_citation":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI/action/citation_signature","submit_replication":"https://pith.science/pith/D5ATQZHVS2FX7IM5F5AJPX7ZSI/action/replication_record"}},"created_at":"2026-05-17T23:46:08.462783+00:00","updated_at":"2026-05-17T23:46:08.462783+00:00"}