{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:P266ULHK4XIDTA4QQRD5LUC7HH","short_pith_number":"pith:P266ULHK","canonical_record":{"source":{"id":"1911.04571","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-11T21:18:53Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"94e25ad703bfb1b7dc5644cd5f0d30f24167533385bd7d19ce68d83267d7040b","abstract_canon_sha256":"8f5964b0a44ebe57d809841c6b9c2fe1c7e319254a9b1c0131d237d9f9e6ee90"},"schema_version":"1.0"},"canonical_sha256":"7ebdea2ceae5d03983908447d5d05f39d497c9fd8b2ddd22665bc08e499bf860","source":{"kind":"arxiv","id":"1911.04571","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.04571","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"arxiv_version","alias_value":"1911.04571v1","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04571","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_12","alias_value":"P266ULHK4XID","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_16","alias_value":"P266ULHK4XIDTA4Q","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_8","alias_value":"P266ULHK","created_at":"2026-07-05T00:18:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:P266ULHK4XIDTA4QQRD5LUC7HH","target":"record","payload":{"canonical_record":{"source":{"id":"1911.04571","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-11T21:18:53Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"94e25ad703bfb1b7dc5644cd5f0d30f24167533385bd7d19ce68d83267d7040b","abstract_canon_sha256":"8f5964b0a44ebe57d809841c6b9c2fe1c7e319254a9b1c0131d237d9f9e6ee90"},"schema_version":"1.0"},"canonical_sha256":"7ebdea2ceae5d03983908447d5d05f39d497c9fd8b2ddd22665bc08e499bf860","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:43.222472Z","signature_b64":"nfT3n3PsqVGbo1q720dm4qX7hgpPTNEzXTtJ6nSlVQL4FwfpMz4vLL9oMkfn84mMSSIuYSLmZ8YZa0sT/GhlBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ebdea2ceae5d03983908447d5d05f39d497c9fd8b2ddd22665bc08e499bf860","last_reissued_at":"2026-07-05T00:18:43.222016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:43.222016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.04571","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":"2026-07-05T00:18:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"77Q1UQ603Fxk4q1Zy7LP0flpqB+vpFRDriYFZ1Zg3AJJlEz+frjGqqHde9Ymb7NFBrlTfT6F2RGG5CxK+Tk2Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T17:59:22.622174Z"},"content_sha256":"b9189b030b0c2fd4c8293339f245640909975cf4ea5d874197ca2ee8a84cd5cd","schema_version":"1.0","event_id":"sha256:b9189b030b0c2fd4c8293339f245640909975cf4ea5d874197ca2ee8a84cd5cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:P266ULHK4XIDTA4QQRD5LUC7HH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Long-span language modeling for speech recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"George Polovets, Sarangarajan Parthasarathy, Shuangyu Chang, William Gale, Xie Chen","submitted_at":"2019-11-11T21:18:53Z","abstract_excerpt":"We explore neural language modeling for speech recognition where the context spans multiple sentences. Rather than encode history beyond the current sentence using a cache of words or document-level features, we focus our study on the ability of LSTM and Transformer language models to implicitly learn to carry over context across sentence boundaries. We introduce a new architecture that incorporates an attention mechanism into LSTM to combine the benefits of recurrent and attention architectures. We conduct language modeling and speech recognition experiments on the publicly available LibriSpe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04571","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/1911.04571/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":"2026-07-05T00:18:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nWDpRqJY40BTAjy6kso9ckEbGQqMxTiFUbMN0iZs7Ge9+R+Ci78ryw0rTJLIZb7Ul/F9Gr6osvM9P1pAYpyfBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T17:59:22.622673Z"},"content_sha256":"04245d61fb9fe6efb23d68635ff4b9a15ef948c031af414730c4f19015d97561","schema_version":"1.0","event_id":"sha256:04245d61fb9fe6efb23d68635ff4b9a15ef948c031af414730c4f19015d97561"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P266ULHK4XIDTA4QQRD5LUC7HH/bundle.json","state_url":"https://pith.science/pith/P266ULHK4XIDTA4QQRD5LUC7HH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P266ULHK4XIDTA4QQRD5LUC7HH/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":"2026-08-15T17:59:22Z","links":{"resolver":"https://pith.science/pith/P266ULHK4XIDTA4QQRD5LUC7HH","bundle":"https://pith.science/pith/P266ULHK4XIDTA4QQRD5LUC7HH/bundle.json","state":"https://pith.science/pith/P266ULHK4XIDTA4QQRD5LUC7HH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P266ULHK4XIDTA4QQRD5LUC7HH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:P266ULHK4XIDTA4QQRD5LUC7HH","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":"8f5964b0a44ebe57d809841c6b9c2fe1c7e319254a9b1c0131d237d9f9e6ee90","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-11T21:18:53Z","title_canon_sha256":"94e25ad703bfb1b7dc5644cd5f0d30f24167533385bd7d19ce68d83267d7040b"},"schema_version":"1.0","source":{"id":"1911.04571","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.04571","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"arxiv_version","alias_value":"1911.04571v1","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04571","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_12","alias_value":"P266ULHK4XID","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_16","alias_value":"P266ULHK4XIDTA4Q","created_at":"2026-07-05T00:18:43Z"},{"alias_kind":"pith_short_8","alias_value":"P266ULHK","created_at":"2026-07-05T00:18:43Z"}],"graph_snapshots":[{"event_id":"sha256:04245d61fb9fe6efb23d68635ff4b9a15ef948c031af414730c4f19015d97561","target":"graph","created_at":"2026-07-05T00:18:43Z","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/1911.04571/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore neural language modeling for speech recognition where the context spans multiple sentences. Rather than encode history beyond the current sentence using a cache of words or document-level features, we focus our study on the ability of LSTM and Transformer language models to implicitly learn to carry over context across sentence boundaries. We introduce a new architecture that incorporates an attention mechanism into LSTM to combine the benefits of recurrent and attention architectures. We conduct language modeling and speech recognition experiments on the publicly available LibriSpe","authors_text":"George Polovets, Sarangarajan Parthasarathy, Shuangyu Chang, William Gale, Xie Chen","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-11T21:18:53Z","title":"Long-span language modeling for speech recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04571","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:b9189b030b0c2fd4c8293339f245640909975cf4ea5d874197ca2ee8a84cd5cd","target":"record","created_at":"2026-07-05T00:18:43Z","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":"8f5964b0a44ebe57d809841c6b9c2fe1c7e319254a9b1c0131d237d9f9e6ee90","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-11T21:18:53Z","title_canon_sha256":"94e25ad703bfb1b7dc5644cd5f0d30f24167533385bd7d19ce68d83267d7040b"},"schema_version":"1.0","source":{"id":"1911.04571","kind":"arxiv","version":1}},"canonical_sha256":"7ebdea2ceae5d03983908447d5d05f39d497c9fd8b2ddd22665bc08e499bf860","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ebdea2ceae5d03983908447d5d05f39d497c9fd8b2ddd22665bc08e499bf860","first_computed_at":"2026-07-05T00:18:43.222016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:18:43.222016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nfT3n3PsqVGbo1q720dm4qX7hgpPTNEzXTtJ6nSlVQL4FwfpMz4vLL9oMkfn84mMSSIuYSLmZ8YZa0sT/GhlBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:18:43.222472Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.04571","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9189b030b0c2fd4c8293339f245640909975cf4ea5d874197ca2ee8a84cd5cd","sha256:04245d61fb9fe6efb23d68635ff4b9a15ef948c031af414730c4f19015d97561"],"state_sha256":"5ae9cf8c7fbe4285ec2f165ad0099af0a996c737de18fb9d5abd479bcbdf7139"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tl8VygGydnq9+ze1JbKyhqSJl9+kLZadK/2Fjvx7BzTcGWTDPq+GLN42e2Lh97RiobC2f24kNayr7wwx366NAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T17:59:22.627066Z","bundle_sha256":"2e1c2cc807f37082ae1eb450be9f6825adc87c4c9ffc28c707555f33f59a9533"}}