{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4EZEWVVNQ3KZUTBOXT3FCNGALZ","short_pith_number":"pith:4EZEWVVN","schema_version":"1.0","canonical_sha256":"e1324b56ad86d59a4c2ebcf65134c05e631189e9287aeb4f4b96fa83a624cd01","source":{"kind":"arxiv","id":"1701.06547","version":5},"attestation_state":"computed","paper":{"title":"Adversarial Learning for Neural Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Ritter, Dan Jurafsky, Jiwei Li, S\\'ebastien Jean, Tianlin Shi, Will Monroe","submitted_at":"2017-01-23T18:32:27Z","abstract_excerpt":"In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as "},"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":"1701.06547","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-01-23T18:32:27Z","cross_cats_sorted":[],"title_canon_sha256":"822edc1a948c1a664cc31e15b1981148744bd415c17cb54e4bfc9d00812a20f2","abstract_canon_sha256":"7b026215b5b172a50acdc72d6fdd25cf2d9ce5d1f2d60402fd411daf4b1ec9fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:31.230363Z","signature_b64":"I1dgMy0CpV9asdrxv0D7iu9Fjg0yZuBJkln5CPttCjPR8t7A35il62xUGRgXTECgLItY/DKdNxJDiJfTf8nfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1324b56ad86d59a4c2ebcf65134c05e631189e9287aeb4f4b96fa83a624cd01","last_reissued_at":"2026-05-18T00:34:31.229766Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:31.229766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Learning for Neural Dialogue Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Ritter, Dan Jurafsky, Jiwei Li, S\\'ebastien Jean, Tianlin Shi, Will Monroe","submitted_at":"2017-01-23T18:32:27Z","abstract_excerpt":"In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.06547","kind":"arxiv","version":5},"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":"1701.06547","created_at":"2026-05-18T00:34:31.229856+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.06547v5","created_at":"2026-05-18T00:34:31.229856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.06547","created_at":"2026-05-18T00:34:31.229856+00:00"},{"alias_kind":"pith_short_12","alias_value":"4EZEWVVNQ3KZ","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"4EZEWVVNQ3KZUTBO","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"4EZEWVVN","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ","json":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ.json","graph_json":"https://pith.science/api/pith-number/4EZEWVVNQ3KZUTBOXT3FCNGALZ/graph.json","events_json":"https://pith.science/api/pith-number/4EZEWVVNQ3KZUTBOXT3FCNGALZ/events.json","paper":"https://pith.science/paper/4EZEWVVN"},"agent_actions":{"view_html":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ","download_json":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ.json","view_paper":"https://pith.science/paper/4EZEWVVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.06547&json=true","fetch_graph":"https://pith.science/api/pith-number/4EZEWVVNQ3KZUTBOXT3FCNGALZ/graph.json","fetch_events":"https://pith.science/api/pith-number/4EZEWVVNQ3KZUTBOXT3FCNGALZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ/action/storage_attestation","attest_author":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ/action/author_attestation","sign_citation":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ/action/citation_signature","submit_replication":"https://pith.science/pith/4EZEWVVNQ3KZUTBOXT3FCNGALZ/action/replication_record"}},"created_at":"2026-05-18T00:34:31.229856+00:00","updated_at":"2026-05-18T00:34:31.229856+00:00"}