{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2PGEQC3IQ6J2R2M65RIEDVGDVO","short_pith_number":"pith:2PGEQC3I","schema_version":"1.0","canonical_sha256":"d3cc480b688793a8e99eec5041d4c3ab82487d558a5296344fff4f6dec9d46df","source":{"kind":"arxiv","id":"1907.12108","version":4},"attestation_state":"computed","paper":{"title":"CAiRE: An Empathetic Neural Chatbot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Farhad Bin Siddique, Genta Indra Winata, Jamin Shin, Pascale Fung, Peng Xu, Zhaojiang Lin, Zihan Liu","submitted_at":"2019-07-28T16:52:09Z","abstract_excerpt":"In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We evaluate our model on the recently proposed empathetic-dialogues dataset (Rashkin et al., 2019), the experiment results show that CAiRE achieves state-of-the-art performance on dialogue emotion detection and empathetic response generation."},"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":"1907.12108","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-28T16:52:09Z","cross_cats_sorted":[],"title_canon_sha256":"5fa6ffdaa89915461a7534c816a75ab2ab1a042bbafc2d770d6696601aa6cd91","abstract_canon_sha256":"59293139eb0242f1ffda08badc9d2ed74a69020e7589f39e2aa545822673054b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:58:04.042441Z","signature_b64":"i+oSqKGqXMox+IWY99oX5Hu849h8wqZzp+M+DwvrhXMUFBXC5kUf0OCH5hfypXktLeFf6FiS2+NAzr2Oo+aqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3cc480b688793a8e99eec5041d4c3ab82487d558a5296344fff4f6dec9d46df","last_reissued_at":"2026-07-05T00:58:04.042081Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:58:04.042081Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAiRE: An Empathetic Neural Chatbot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Farhad Bin Siddique, Genta Indra Winata, Jamin Shin, Pascale Fung, Peng Xu, Zhaojiang Lin, Zihan Liu","submitted_at":"2019-07-28T16:52:09Z","abstract_excerpt":"In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We evaluate our model on the recently proposed empathetic-dialogues dataset (Rashkin et al., 2019), the experiment results show that CAiRE achieves state-of-the-art performance on dialogue emotion detection and empathetic response generation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.12108","kind":"arxiv","version":4},"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/1907.12108/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":"1907.12108","created_at":"2026-07-05T00:58:04.042143+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.12108v4","created_at":"2026-07-05T00:58:04.042143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.12108","created_at":"2026-07-05T00:58:04.042143+00:00"},{"alias_kind":"pith_short_12","alias_value":"2PGEQC3IQ6J2","created_at":"2026-07-05T00:58:04.042143+00:00"},{"alias_kind":"pith_short_16","alias_value":"2PGEQC3IQ6J2R2M6","created_at":"2026-07-05T00:58:04.042143+00:00"},{"alias_kind":"pith_short_8","alias_value":"2PGEQC3I","created_at":"2026-07-05T00:58:04.042143+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.04492","citing_title":"Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO","json":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO.json","graph_json":"https://pith.science/api/pith-number/2PGEQC3IQ6J2R2M65RIEDVGDVO/graph.json","events_json":"https://pith.science/api/pith-number/2PGEQC3IQ6J2R2M65RIEDVGDVO/events.json","paper":"https://pith.science/paper/2PGEQC3I"},"agent_actions":{"view_html":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO","download_json":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO.json","view_paper":"https://pith.science/paper/2PGEQC3I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.12108&json=true","fetch_graph":"https://pith.science/api/pith-number/2PGEQC3IQ6J2R2M65RIEDVGDVO/graph.json","fetch_events":"https://pith.science/api/pith-number/2PGEQC3IQ6J2R2M65RIEDVGDVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO/action/storage_attestation","attest_author":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO/action/author_attestation","sign_citation":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO/action/citation_signature","submit_replication":"https://pith.science/pith/2PGEQC3IQ6J2R2M65RIEDVGDVO/action/replication_record"}},"created_at":"2026-07-05T00:58:04.042143+00:00","updated_at":"2026-07-05T00:58:04.042143+00:00"}