{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ADOZVDKGYIHW2ONXJIQTG4426P","short_pith_number":"pith:ADOZVDKG","schema_version":"1.0","canonical_sha256":"00dd9a8d46c20f6d39b74a2133739af3d0b882efb3559943793e381a372cab21","source":{"kind":"arxiv","id":"1705.11001","version":3},"attestation_state":"computed","paper":{"title":"Adversarial Ranking for Language Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Dianqi Li, Kevin Lin, Ming-Ting Sun, Xiaodong He, Zhengyou Zhang","submitted_at":"2017-05-31T09:21:04Z","abstract_excerpt":"Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collect"},"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":"1705.11001","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-31T09:21:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"abf342ca6b9340899e4e7d9f66a869aaf760c7c771a5c05a951672f8b3fa932e","abstract_canon_sha256":"ee4de6688ffcb3af74a50a0a4cef02e02d7c8c42233230429acd6f62f902b5c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:31.817135Z","signature_b64":"qrnD29x7t3zoq9/qpMVdEqMEPXaR0c2Z7+NE+sldZgocs3TD3RYPTRQEnJbYIhqwH0BZ+0owCe6ADx1yZrwyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00dd9a8d46c20f6d39b74a2133739af3d0b882efb3559943793e381a372cab21","last_reissued_at":"2026-05-18T00:18:31.816579Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:31.816579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Ranking for Language Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Dianqi Li, Kevin Lin, Ming-Ting Sun, Xiaodong He, Zhengyou Zhang","submitted_at":"2017-05-31T09:21:04Z","abstract_excerpt":"Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.11001","kind":"arxiv","version":3},"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":"1705.11001","created_at":"2026-05-18T00:18:31.816670+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.11001v3","created_at":"2026-05-18T00:18:31.816670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.11001","created_at":"2026-05-18T00:18:31.816670+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADOZVDKGYIHW","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADOZVDKGYIHW2ONX","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADOZVDKG","created_at":"2026-05-18T12:31:05.417338+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/ADOZVDKGYIHW2ONXJIQTG4426P","json":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P.json","graph_json":"https://pith.science/api/pith-number/ADOZVDKGYIHW2ONXJIQTG4426P/graph.json","events_json":"https://pith.science/api/pith-number/ADOZVDKGYIHW2ONXJIQTG4426P/events.json","paper":"https://pith.science/paper/ADOZVDKG"},"agent_actions":{"view_html":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P","download_json":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P.json","view_paper":"https://pith.science/paper/ADOZVDKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.11001&json=true","fetch_graph":"https://pith.science/api/pith-number/ADOZVDKGYIHW2ONXJIQTG4426P/graph.json","fetch_events":"https://pith.science/api/pith-number/ADOZVDKGYIHW2ONXJIQTG4426P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P/action/storage_attestation","attest_author":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P/action/author_attestation","sign_citation":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P/action/citation_signature","submit_replication":"https://pith.science/pith/ADOZVDKGYIHW2ONXJIQTG4426P/action/replication_record"}},"created_at":"2026-05-18T00:18:31.816670+00:00","updated_at":"2026-05-18T00:18:31.816670+00:00"}