{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:D7RHQIKQYOGHTSSZCPNNCVG2UC","short_pith_number":"pith:D7RHQIKQ","canonical_record":{"source":{"id":"2007.08557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-07-09T04:34:48Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG","stat.ML"],"title_canon_sha256":"ef30f56e478ec94b12f6e975be29a9bb7228297e7310fb9ee7b49715281fd850","abstract_canon_sha256":"a2ca682404d5b126f8d03ca973c751bc0de9292ab855a5031bae8fe05aecca95"},"schema_version":"1.0"},"canonical_sha256":"1fe2782150c38c79ca5913dad154daa092f7653025085c7e39de8fc32976ec76","source":{"kind":"arxiv","id":"2007.08557","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.08557","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"2007.08557v1","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.08557","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"D7RHQIKQYOGH","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_16","alias_value":"D7RHQIKQYOGHTSSZ","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_8","alias_value":"D7RHQIKQ","created_at":"2026-07-05T01:19:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:D7RHQIKQYOGHTSSZCPNNCVG2UC","target":"record","payload":{"canonical_record":{"source":{"id":"2007.08557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-07-09T04:34:48Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG","stat.ML"],"title_canon_sha256":"ef30f56e478ec94b12f6e975be29a9bb7228297e7310fb9ee7b49715281fd850","abstract_canon_sha256":"a2ca682404d5b126f8d03ca973c751bc0de9292ab855a5031bae8fe05aecca95"},"schema_version":"1.0"},"canonical_sha256":"1fe2782150c38c79ca5913dad154daa092f7653025085c7e39de8fc32976ec76","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:54.225617Z","signature_b64":"FsgMt0T7IANhJ5w4440E6xP36FrrjQlIbROYtbgcR5a43zxM1L6zncT3H95fek4Yl/ZfEKJJ5W61nJV10rJkBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fe2782150c38c79ca5913dad154daa092f7653025085c7e39de8fc32976ec76","last_reissued_at":"2026-07-05T01:19:54.225053Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:54.225053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.08557","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:19:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mm2U5TZYGEJ421z67+QMF7XWwrFtH464EJ/xhgkUzTupKeirFUzR0z8NUa5Ypd3Xgo2eQ7istWcP/+BQsPZIBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T04:24:39.313015Z"},"content_sha256":"21987353242c6bc9c16466ed2d6ba691e17c22c3038739859af67fca4d3ae44b","schema_version":"1.0","event_id":"sha256:21987353242c6bc9c16466ed2d6ba691e17c22c3038739859af67fca4d3ae44b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:D7RHQIKQYOGHTSSZCPNNCVG2UC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Text Generation by Learning from Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Irwin King, Jingjing Li, Lili Mou, Michael R. Lyu, Xin Jiang, Zichao Li","submitted_at":"2020-07-09T04:34:48Z","abstract_excerpt":"In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search. We start by applying a strong search algorithm (in particular, simulated annealing) towards a heuristically defined objective that (roughly) estimates the quality of sentences. Then, a conditional generative model learns from the search results, and meanwhile smooth out the noise of search. The alternation between search and learning can be repeated for performance bootstrapping. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.08557","kind":"arxiv","version":1},"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/2007.08557/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:19:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vBCTb6MQ4Z0WoR3a6VN6V4O7k5DpLmn2oowHQ9eIZpYeOHeix7lVry/x2Tn3ENRhLZ8UdskgycmYKO+BWcIlBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T04:24:39.313400Z"},"content_sha256":"eaf9f2408253bf07cd6e877cbc0cc637ff5bc81f8edbad6b9124790a2e6e2ad0","schema_version":"1.0","event_id":"sha256:eaf9f2408253bf07cd6e877cbc0cc637ff5bc81f8edbad6b9124790a2e6e2ad0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/bundle.json","state_url":"https://pith.science/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-26T04:24:39Z","links":{"resolver":"https://pith.science/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC","bundle":"https://pith.science/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/bundle.json","state":"https://pith.science/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D7RHQIKQYOGHTSSZCPNNCVG2UC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:D7RHQIKQYOGHTSSZCPNNCVG2UC","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":"a2ca682404d5b126f8d03ca973c751bc0de9292ab855a5031bae8fe05aecca95","cross_cats_sorted":["cs.AI","cs.IR","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-07-09T04:34:48Z","title_canon_sha256":"ef30f56e478ec94b12f6e975be29a9bb7228297e7310fb9ee7b49715281fd850"},"schema_version":"1.0","source":{"id":"2007.08557","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.08557","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"2007.08557v1","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.08557","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"D7RHQIKQYOGH","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_16","alias_value":"D7RHQIKQYOGHTSSZ","created_at":"2026-07-05T01:19:54Z"},{"alias_kind":"pith_short_8","alias_value":"D7RHQIKQ","created_at":"2026-07-05T01:19:54Z"}],"graph_snapshots":[{"event_id":"sha256:eaf9f2408253bf07cd6e877cbc0cc637ff5bc81f8edbad6b9124790a2e6e2ad0","target":"graph","created_at":"2026-07-05T01:19:54Z","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/2007.08557/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search. We start by applying a strong search algorithm (in particular, simulated annealing) towards a heuristically defined objective that (roughly) estimates the quality of sentences. Then, a conditional generative model learns from the search results, and meanwhile smooth out the noise of search. The alternation between search and learning can be repeated for performance bootstrapping. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase genera","authors_text":"Irwin King, Jingjing Li, Lili Mou, Michael R. Lyu, Xin Jiang, Zichao Li","cross_cats":["cs.AI","cs.IR","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-07-09T04:34:48Z","title":"Unsupervised Text Generation by Learning from Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.08557","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":"sha256:21987353242c6bc9c16466ed2d6ba691e17c22c3038739859af67fca4d3ae44b","target":"record","created_at":"2026-07-05T01:19:54Z","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":"a2ca682404d5b126f8d03ca973c751bc0de9292ab855a5031bae8fe05aecca95","cross_cats_sorted":["cs.AI","cs.IR","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-07-09T04:34:48Z","title_canon_sha256":"ef30f56e478ec94b12f6e975be29a9bb7228297e7310fb9ee7b49715281fd850"},"schema_version":"1.0","source":{"id":"2007.08557","kind":"arxiv","version":1}},"canonical_sha256":"1fe2782150c38c79ca5913dad154daa092f7653025085c7e39de8fc32976ec76","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1fe2782150c38c79ca5913dad154daa092f7653025085c7e39de8fc32976ec76","first_computed_at":"2026-07-05T01:19:54.225053Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:19:54.225053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FsgMt0T7IANhJ5w4440E6xP36FrrjQlIbROYtbgcR5a43zxM1L6zncT3H95fek4Yl/ZfEKJJ5W61nJV10rJkBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:19:54.225617Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.08557","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21987353242c6bc9c16466ed2d6ba691e17c22c3038739859af67fca4d3ae44b","sha256:eaf9f2408253bf07cd6e877cbc0cc637ff5bc81f8edbad6b9124790a2e6e2ad0"],"state_sha256":"c51d735f179d85087d3c10039959110f51728c4f0b3ebd1cb1368efce840fea1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FxRf+CTYbe5cEY/10wIRBdGJg749mBgmlspagiW5I/J0BBWN2qn3gRZ834D5m4df8zzsheHsSvA4E8A7xyG4CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-26T04:24:39.315905Z","bundle_sha256":"4ed7866fd370df88ab750fd5d362b77708a7f74a1829b781e72a21de8b53b717"}}