{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ECPQH7HPTVLCEIIO7HY6HQ6UEL","short_pith_number":"pith:ECPQH7HP","schema_version":"1.0","canonical_sha256":"209f03fcef9d5622210ef9f1e3c3d422f490a9406d0ecdffe432599e7f1bbfce","source":{"kind":"arxiv","id":"1901.08149","version":2},"attestation_state":"computed","paper":{"title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Clement Delangue, Julien Chaumond, Thomas Wolf, Victor Sanh","submitted_at":"2019-01-23T22:08:01Z","abstract_excerpt":"We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challe"},"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":"1901.08149","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-01-23T22:08:01Z","cross_cats_sorted":[],"title_canon_sha256":"b60b66c1ee7268de9b552129c990063f746303de72b80865f1c36639369d3d68","abstract_canon_sha256":"c449f9991a1a78bba04e4c5278fbedef090166a6b4721a7bf54853be481c6731"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:53.103133Z","signature_b64":"BdPhwZdYo0MhOopGixBrY1DQ7rnVQ+dp18L7VebqgljXJO2uiCW617WnsZ4s3UTNXgMFCtNIaDsNO8hBmC/EBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"209f03fcef9d5622210ef9f1e3c3d422f490a9406d0ecdffe432599e7f1bbfce","last_reissued_at":"2026-05-17T23:54:53.102686Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:53.102686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Clement Delangue, Julien Chaumond, Thomas Wolf, Victor Sanh","submitted_at":"2019-01-23T22:08:01Z","abstract_excerpt":"We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08149","kind":"arxiv","version":2},"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":"1901.08149","created_at":"2026-05-17T23:54:53.102755+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.08149v2","created_at":"2026-05-17T23:54:53.102755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08149","created_at":"2026-05-17T23:54:53.102755+00:00"},{"alias_kind":"pith_short_12","alias_value":"ECPQH7HPTVLC","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"ECPQH7HPTVLCEIIO","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"ECPQH7HP","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07788","citing_title":"PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL","json":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL.json","graph_json":"https://pith.science/api/pith-number/ECPQH7HPTVLCEIIO7HY6HQ6UEL/graph.json","events_json":"https://pith.science/api/pith-number/ECPQH7HPTVLCEIIO7HY6HQ6UEL/events.json","paper":"https://pith.science/paper/ECPQH7HP"},"agent_actions":{"view_html":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL","download_json":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL.json","view_paper":"https://pith.science/paper/ECPQH7HP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.08149&json=true","fetch_graph":"https://pith.science/api/pith-number/ECPQH7HPTVLCEIIO7HY6HQ6UEL/graph.json","fetch_events":"https://pith.science/api/pith-number/ECPQH7HPTVLCEIIO7HY6HQ6UEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL/action/storage_attestation","attest_author":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL/action/author_attestation","sign_citation":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL/action/citation_signature","submit_replication":"https://pith.science/pith/ECPQH7HPTVLCEIIO7HY6HQ6UEL/action/replication_record"}},"created_at":"2026-05-17T23:54:53.102755+00:00","updated_at":"2026-05-17T23:54:53.102755+00:00"}