{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CIV63MJYTNHVI64M7HDYKLCA2U","short_pith_number":"pith:CIV63MJY","canonical_record":{"source":{"id":"1808.09111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T04:33:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"996b2b8205077d7203a069ce8127cc26ab406d1d6f442f89a36b0d81ca94e1fb","abstract_canon_sha256":"a12dcfe5f8f0495ab216da58559156395c4597a157fd13213c0f06a048e3fafd"},"schema_version":"1.0"},"canonical_sha256":"122bedb1389b4f547b8cf9c7852c40d51adfd93ebb2984e841c1c18f49c8139a","source":{"kind":"arxiv","id":"1808.09111","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.09111","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"arxiv_version","alias_value":"1808.09111v1","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09111","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"pith_short_12","alias_value":"CIV63MJYTNHV","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CIV63MJYTNHVI64M","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CIV63MJY","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CIV63MJYTNHVI64M7HDYKLCA2U","target":"record","payload":{"canonical_record":{"source":{"id":"1808.09111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T04:33:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"996b2b8205077d7203a069ce8127cc26ab406d1d6f442f89a36b0d81ca94e1fb","abstract_canon_sha256":"a12dcfe5f8f0495ab216da58559156395c4597a157fd13213c0f06a048e3fafd"},"schema_version":"1.0"},"canonical_sha256":"122bedb1389b4f547b8cf9c7852c40d51adfd93ebb2984e841c1c18f49c8139a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:04.457804Z","signature_b64":"PdBSRDMzk5pDgYwRxxPqmldHvA3zYcKlru+af7l7yybCCdZLtccPttIbcWNlZ8YiOhxpCC/pJi4nCxSiHUm+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"122bedb1389b4f547b8cf9c7852c40d51adfd93ebb2984e841c1c18f49c8139a","last_reissued_at":"2026-05-18T00:07:04.457112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:04.457112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.09111","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":"2026-05-18T00:07:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EXCrBtx6FS5Kc3c12Ah0ONoETT6bwrhg32pTMX9FYyb8Y5evaR4RtphEm2nthe2zofmnUwdZJIsv+1X/bvCwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T06:46:52.216628Z"},"content_sha256":"ea551c76bef25cc744940aacc83c3eda0a69b150828ecbdebac2de334e6deae9","schema_version":"1.0","event_id":"sha256:ea551c76bef25cc744940aacc83c3eda0a69b150828ecbdebac2de334e6deae9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CIV63MJYTNHVI64M7HDYKLCA2U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Learning of Syntactic Structure with Invertible Neural Projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Graham Neubig, Junxian He, Taylor Berg-Kirkpatrick","submitted_at":"2018-08-28T04:33:25Z","abstract_excerpt":"Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09111","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":""},"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":"2026-05-18T00:07:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eT83TJYdUOLwjOWRg3jzjdsvwkWsrP24xNsOmx+1HcwHi2RvVgW5iRrBkTMADqgRb1SWoINhNZRLRNbDUTl9Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T06:46:52.216974Z"},"content_sha256":"1dd398f78b94afcce3e8a99b8b70ac96500c1a990ecd2aeed8c3d14245050806","schema_version":"1.0","event_id":"sha256:1dd398f78b94afcce3e8a99b8b70ac96500c1a990ecd2aeed8c3d14245050806"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CIV63MJYTNHVI64M7HDYKLCA2U/bundle.json","state_url":"https://pith.science/pith/CIV63MJYTNHVI64M7HDYKLCA2U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CIV63MJYTNHVI64M7HDYKLCA2U/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":"2026-06-04T06:46:52Z","links":{"resolver":"https://pith.science/pith/CIV63MJYTNHVI64M7HDYKLCA2U","bundle":"https://pith.science/pith/CIV63MJYTNHVI64M7HDYKLCA2U/bundle.json","state":"https://pith.science/pith/CIV63MJYTNHVI64M7HDYKLCA2U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CIV63MJYTNHVI64M7HDYKLCA2U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CIV63MJYTNHVI64M7HDYKLCA2U","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":"a12dcfe5f8f0495ab216da58559156395c4597a157fd13213c0f06a048e3fafd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T04:33:25Z","title_canon_sha256":"996b2b8205077d7203a069ce8127cc26ab406d1d6f442f89a36b0d81ca94e1fb"},"schema_version":"1.0","source":{"id":"1808.09111","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.09111","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"arxiv_version","alias_value":"1808.09111v1","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09111","created_at":"2026-05-18T00:07:04Z"},{"alias_kind":"pith_short_12","alias_value":"CIV63MJYTNHV","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CIV63MJYTNHVI64M","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CIV63MJY","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:1dd398f78b94afcce3e8a99b8b70ac96500c1a990ecd2aeed8c3d14245050806","target":"graph","created_at":"2026-05-18T00:07:04Z","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"},"paper":{"abstract_excerpt":"Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so","authors_text":"Graham Neubig, Junxian He, Taylor Berg-Kirkpatrick","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T04:33:25Z","title":"Unsupervised Learning of Syntactic Structure with Invertible Neural Projections"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09111","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:ea551c76bef25cc744940aacc83c3eda0a69b150828ecbdebac2de334e6deae9","target":"record","created_at":"2026-05-18T00:07:04Z","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":"a12dcfe5f8f0495ab216da58559156395c4597a157fd13213c0f06a048e3fafd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T04:33:25Z","title_canon_sha256":"996b2b8205077d7203a069ce8127cc26ab406d1d6f442f89a36b0d81ca94e1fb"},"schema_version":"1.0","source":{"id":"1808.09111","kind":"arxiv","version":1}},"canonical_sha256":"122bedb1389b4f547b8cf9c7852c40d51adfd93ebb2984e841c1c18f49c8139a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"122bedb1389b4f547b8cf9c7852c40d51adfd93ebb2984e841c1c18f49c8139a","first_computed_at":"2026-05-18T00:07:04.457112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:04.457112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PdBSRDMzk5pDgYwRxxPqmldHvA3zYcKlru+af7l7yybCCdZLtccPttIbcWNlZ8YiOhxpCC/pJi4nCxSiHUm+DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:04.457804Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.09111","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea551c76bef25cc744940aacc83c3eda0a69b150828ecbdebac2de334e6deae9","sha256:1dd398f78b94afcce3e8a99b8b70ac96500c1a990ecd2aeed8c3d14245050806"],"state_sha256":"2f56c008f0e84419b2f4db19ad2aea106fb2141b37a3bcbe57637c03c72cf8b2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zbn2+3q3AGAEnDID/ukGArjrd67m8ZNUxyqVSEWb70k63ibtoFXLdEa6LvTCKlZemj27SAvplXvRna6mZELuBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T06:46:52.218952Z","bundle_sha256":"f82a632570d55d48b600be35c5ee0c2af2451e316f14840326d50fd73c7a6132"}}