{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5DXM6OLJAIGFZW4CUHDQECDHNO","short_pith_number":"pith:5DXM6OLJ","canonical_record":{"source":{"id":"1803.03585","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-09T16:13:02Z","cross_cats_sorted":[],"title_canon_sha256":"2afd407172b7fc9301c315a1ec95520a6be8d534d080ec001cfbfbee0b82b6ac","abstract_canon_sha256":"837917d9fa1408131dd7fe01056d0f18b006c5ef17bbab66d57b9eaeace76e21"},"schema_version":"1.0"},"canonical_sha256":"e8eecf3969020c5cdb82a1c70208676b99adfb63aff47fe903806cfd2c475c95","source":{"kind":"arxiv","id":"1803.03585","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03585","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03585v2","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03585","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"pith_short_12","alias_value":"5DXM6OLJAIGF","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5DXM6OLJAIGFZW4C","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5DXM6OLJ","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5DXM6OLJAIGFZW4CUHDQECDHNO","target":"record","payload":{"canonical_record":{"source":{"id":"1803.03585","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-09T16:13:02Z","cross_cats_sorted":[],"title_canon_sha256":"2afd407172b7fc9301c315a1ec95520a6be8d534d080ec001cfbfbee0b82b6ac","abstract_canon_sha256":"837917d9fa1408131dd7fe01056d0f18b006c5ef17bbab66d57b9eaeace76e21"},"schema_version":"1.0"},"canonical_sha256":"e8eecf3969020c5cdb82a1c70208676b99adfb63aff47fe903806cfd2c475c95","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:10.469422Z","signature_b64":"cELPfMhVHmSoSeHXnkBzJra/B2v9/QroDBegsmqMDanuyTpAITIMKLeiVW0Vd6x0NRBJ2Wju46Q99ZQp4MgOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8eecf3969020c5cdb82a1c70208676b99adfb63aff47fe903806cfd2c475c95","last_reissued_at":"2026-05-18T00:07:10.468918Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:10.468918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.03585","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S/D0EdQk3nWkPXaTSigD57S+zmPRQxT7YCMUjamtRwSEoiEMApC9I46m7RoqqJnGLrm0I0A7rXpUhHlSxGQQCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:53:03.709153Z"},"content_sha256":"4175bda222404b9a80f245a353f522fbb1bd97995976481493be35d5dbd14cea","schema_version":"1.0","event_id":"sha256:4175bda222404b9a80f245a353f522fbb1bd97995976481493be35d5dbd14cea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5DXM6OLJAIGFZW4CUHDQECDHNO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Importance of Being Recurrent for Modeling Hierarchical Structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arianna Bisazza, Christof Monz, Ke Tran","submitted_at":"2018-03-09T16:13:02Z","abstract_excerpt":"Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures---recurrent versus non-recurrent---with respect to their ability to mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03585","kind":"arxiv","version":2},"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":"2026-05-18T00:07:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cjci207RQa5CV7tnGb+1GCTDn/W98R6X3yXwn2MWoxgluIQ0zZtye9SYkNy8zeer4EzDcKkJ93tU2Pz8MX+TBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:53:03.709865Z"},"content_sha256":"58fec0d618b8a5c71674b05c778d64b23c046456a3f4c5f4c3b469262032ab56","schema_version":"1.0","event_id":"sha256:58fec0d618b8a5c71674b05c778d64b23c046456a3f4c5f4c3b469262032ab56"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/bundle.json","state_url":"https://pith.science/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/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":"2026-05-26T03:53:03Z","links":{"resolver":"https://pith.science/pith/5DXM6OLJAIGFZW4CUHDQECDHNO","bundle":"https://pith.science/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/bundle.json","state":"https://pith.science/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5DXM6OLJAIGFZW4CUHDQECDHNO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5DXM6OLJAIGFZW4CUHDQECDHNO","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":"837917d9fa1408131dd7fe01056d0f18b006c5ef17bbab66d57b9eaeace76e21","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-09T16:13:02Z","title_canon_sha256":"2afd407172b7fc9301c315a1ec95520a6be8d534d080ec001cfbfbee0b82b6ac"},"schema_version":"1.0","source":{"id":"1803.03585","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03585","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03585v2","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03585","created_at":"2026-05-18T00:07:10Z"},{"alias_kind":"pith_short_12","alias_value":"5DXM6OLJAIGF","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5DXM6OLJAIGFZW4C","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5DXM6OLJ","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:58fec0d618b8a5c71674b05c778d64b23c046456a3f4c5f4c3b469262032ab56","target":"graph","created_at":"2026-05-18T00:07:10Z","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":"Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures---recurrent versus non-recurrent---with respect to their ability to mo","authors_text":"Arianna Bisazza, Christof Monz, Ke Tran","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-09T16:13:02Z","title":"The Importance of Being Recurrent for Modeling Hierarchical Structure"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03585","kind":"arxiv","version":2},"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":"sha256:4175bda222404b9a80f245a353f522fbb1bd97995976481493be35d5dbd14cea","target":"record","created_at":"2026-05-18T00:07:10Z","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":"837917d9fa1408131dd7fe01056d0f18b006c5ef17bbab66d57b9eaeace76e21","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-09T16:13:02Z","title_canon_sha256":"2afd407172b7fc9301c315a1ec95520a6be8d534d080ec001cfbfbee0b82b6ac"},"schema_version":"1.0","source":{"id":"1803.03585","kind":"arxiv","version":2}},"canonical_sha256":"e8eecf3969020c5cdb82a1c70208676b99adfb63aff47fe903806cfd2c475c95","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e8eecf3969020c5cdb82a1c70208676b99adfb63aff47fe903806cfd2c475c95","first_computed_at":"2026-05-18T00:07:10.468918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:10.468918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cELPfMhVHmSoSeHXnkBzJra/B2v9/QroDBegsmqMDanuyTpAITIMKLeiVW0Vd6x0NRBJ2Wju46Q99ZQp4MgOBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:10.469422Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.03585","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4175bda222404b9a80f245a353f522fbb1bd97995976481493be35d5dbd14cea","sha256:58fec0d618b8a5c71674b05c778d64b23c046456a3f4c5f4c3b469262032ab56"],"state_sha256":"05cff83f251f373b7167de25de1198638b233234c87558c1a3208a4e03e7ba47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M4ehWAhYLj3H7aU6pfBvqIIR8wHvB0rRAlovEWJSeYx7hvurHMZXguAz1VIglq4mdOCM0FTZCYltlbWkDiOjDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:53:03.713595Z","bundle_sha256":"e5fed6c41ab04e599ece4577b59c3ca3e799c42efc69b6c82064cd88b8e281f3"}}