{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:4JKA73VINHKZHZ4VE5QT56LPL6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4654d22123e18651be82c7be54a031bff42ce5f528251f8eb8a3f5be3d735e96","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-04-22T23:19:55Z","title_canon_sha256":"3a86d39f327c10120dcc8a1c5308bcd7239ec8f4edf1822818f0cbfcd361508e"},"schema_version":"1.0","source":{"id":"2004.11714","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.11714","created_at":"2026-07-05T00:57:56Z"},{"alias_kind":"arxiv_version","alias_value":"2004.11714v1","created_at":"2026-07-05T00:57:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11714","created_at":"2026-07-05T00:57:56Z"},{"alias_kind":"pith_short_12","alias_value":"4JKA73VINHKZ","created_at":"2026-07-05T00:57:56Z"},{"alias_kind":"pith_short_16","alias_value":"4JKA73VINHKZHZ4V","created_at":"2026-07-05T00:57:56Z"},{"alias_kind":"pith_short_8","alias_value":"4JKA73VI","created_at":"2026-07-05T00:57:56Z"}],"graph_snapshots":[{"event_id":"sha256:bad2b2a2c67162a055d1a0a4956abe97e298b31fcb408fc5ef3dfc168f325631","target":"graph","created_at":"2026-07-05T00:57:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2004.11714/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process. In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level. In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train usin","authors_text":"Anton Bakhtin, Arthur Szlam, Marc'Aurelio Ranzato, Myle Ott, Yuntian Deng","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-04-22T23:19:55Z","title":"Residual Energy-Based Models for Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11714","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b888def26e6f2a891aa54fbb8b1b76bd98fe70f06ee14d50c69ddaf16be8da2b","target":"record","created_at":"2026-07-05T00:57:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4654d22123e18651be82c7be54a031bff42ce5f528251f8eb8a3f5be3d735e96","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-04-22T23:19:55Z","title_canon_sha256":"3a86d39f327c10120dcc8a1c5308bcd7239ec8f4edf1822818f0cbfcd361508e"},"schema_version":"1.0","source":{"id":"2004.11714","kind":"arxiv","version":1}},"canonical_sha256":"e2540feea869d593e79527613ef96f5f8a1e132bfe3a34b9b4bd4c914f374605","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2540feea869d593e79527613ef96f5f8a1e132bfe3a34b9b4bd4c914f374605","first_computed_at":"2026-07-05T00:57:56.919358Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:57:56.919358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z1zjq6z60UG4+RlUp9Bu9B9wgS2UCUplSazAqiVgwtBHTlHQw7eYUwuTXelNU62v1Wu3RJjEoo01vT9Gm4N2DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:57:56.919847Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.11714","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b888def26e6f2a891aa54fbb8b1b76bd98fe70f06ee14d50c69ddaf16be8da2b","sha256:bad2b2a2c67162a055d1a0a4956abe97e298b31fcb408fc5ef3dfc168f325631"],"state_sha256":"8f1e5473b81b821b39d6fb7fdb8418738f87fad5f8a891dd676260d66db9a514"}