{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TBXS4UHPWBBWZABTEGPMJDDSMD","short_pith_number":"pith:TBXS4UHP","canonical_record":{"source":{"id":"1812.07807","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T08:35:38Z","cross_cats_sorted":[],"title_canon_sha256":"af9e4cb74cf85071e6be55a0a036dd492a36f20925e65fcb610bd310b76f438c","abstract_canon_sha256":"43709662989539fcfae94cb0a6f514eaebc71fd44e61568d765d52219f2dd644"},"schema_version":"1.0"},"canonical_sha256":"986f2e50efb0436c8033219ec48c7260e38f10030a67981c38906098cb9eb6ac","source":{"kind":"arxiv","id":"1812.07807","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07807","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07807v2","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07807","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"pith_short_12","alias_value":"TBXS4UHPWBBW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TBXS4UHPWBBWZABT","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TBXS4UHP","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TBXS4UHPWBBWZABTEGPMJDDSMD","target":"record","payload":{"canonical_record":{"source":{"id":"1812.07807","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T08:35:38Z","cross_cats_sorted":[],"title_canon_sha256":"af9e4cb74cf85071e6be55a0a036dd492a36f20925e65fcb610bd310b76f438c","abstract_canon_sha256":"43709662989539fcfae94cb0a6f514eaebc71fd44e61568d765d52219f2dd644"},"schema_version":"1.0"},"canonical_sha256":"986f2e50efb0436c8033219ec48c7260e38f10030a67981c38906098cb9eb6ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:33.884903Z","signature_b64":"DrOq6+feQ7Y84NudrdI4UQL1nVpYK75n7yp5+hS+B05gByYY3Bk/UG2vMHQuJgwS60m5AT9zAN7mTLAmXzObCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"986f2e50efb0436c8033219ec48c7260e38f10030a67981c38906098cb9eb6ac","last_reissued_at":"2026-05-17T23:40:33.884342Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:33.884342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.07807","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":"2026-05-17T23:40:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ye1v0SapsF4b4RgieU4R8YqJKjUgLPLqEmfvOhgAGPbQz7I+tVaA3d+UqWlnxeAdv8aCath8wOxzjMexfICFBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:05:24.183511Z"},"content_sha256":"dfb33d82d27c14a76ab764964f442b5ef5c7afc33b7e6c3c6435144acf28c37a","schema_version":"1.0","event_id":"sha256:dfb33d82d27c14a76ab764964f442b5ef5c7afc33b7e6c3c6435144acf28c37a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TBXS4UHPWBBWZABTEGPMJDDSMD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DTMT: A Novel Deep Transition Architecture for Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fandong Meng, Jinchao Zhang","submitted_at":"2018-12-19T08:35:38Z","abstract_excerpt":"Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a no"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07807","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":"2026-05-17T23:40:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xKrvEK7Nyw4nRzb7sY/WlqVHqf+my9vuaVgCHy6zKdcR/ql/rWH4JO/gR3de9t2sUSRSpuhz0FEyvwrB/tBfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:05:24.184312Z"},"content_sha256":"5262753770bf6ef281cb61936ae95cea0af286ce3fb78cdd4d9ebe70a3504293","schema_version":"1.0","event_id":"sha256:5262753770bf6ef281cb61936ae95cea0af286ce3fb78cdd4d9ebe70a3504293"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/bundle.json","state_url":"https://pith.science/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/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":"2026-08-16T12:05:24Z","links":{"resolver":"https://pith.science/pith/TBXS4UHPWBBWZABTEGPMJDDSMD","bundle":"https://pith.science/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/bundle.json","state":"https://pith.science/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TBXS4UHPWBBWZABTEGPMJDDSMD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TBXS4UHPWBBWZABTEGPMJDDSMD","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":"43709662989539fcfae94cb0a6f514eaebc71fd44e61568d765d52219f2dd644","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T08:35:38Z","title_canon_sha256":"af9e4cb74cf85071e6be55a0a036dd492a36f20925e65fcb610bd310b76f438c"},"schema_version":"1.0","source":{"id":"1812.07807","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.07807","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"arxiv_version","alias_value":"1812.07807v2","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07807","created_at":"2026-05-17T23:40:33Z"},{"alias_kind":"pith_short_12","alias_value":"TBXS4UHPWBBW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TBXS4UHPWBBWZABT","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TBXS4UHP","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:5262753770bf6ef281cb61936ae95cea0af286ce3fb78cdd4d9ebe70a3504293","target":"graph","created_at":"2026-05-17T23:40:33Z","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":"Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a no","authors_text":"Fandong Meng, Jinchao Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T08:35:38Z","title":"DTMT: A Novel Deep Transition Architecture for Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07807","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:dfb33d82d27c14a76ab764964f442b5ef5c7afc33b7e6c3c6435144acf28c37a","target":"record","created_at":"2026-05-17T23:40:33Z","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":"43709662989539fcfae94cb0a6f514eaebc71fd44e61568d765d52219f2dd644","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-19T08:35:38Z","title_canon_sha256":"af9e4cb74cf85071e6be55a0a036dd492a36f20925e65fcb610bd310b76f438c"},"schema_version":"1.0","source":{"id":"1812.07807","kind":"arxiv","version":2}},"canonical_sha256":"986f2e50efb0436c8033219ec48c7260e38f10030a67981c38906098cb9eb6ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"986f2e50efb0436c8033219ec48c7260e38f10030a67981c38906098cb9eb6ac","first_computed_at":"2026-05-17T23:40:33.884342Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:33.884342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DrOq6+feQ7Y84NudrdI4UQL1nVpYK75n7yp5+hS+B05gByYY3Bk/UG2vMHQuJgwS60m5AT9zAN7mTLAmXzObCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:33.884903Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.07807","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfb33d82d27c14a76ab764964f442b5ef5c7afc33b7e6c3c6435144acf28c37a","sha256:5262753770bf6ef281cb61936ae95cea0af286ce3fb78cdd4d9ebe70a3504293"],"state_sha256":"3c2722ccd7af151ff13a5540d6652f057de0c38055475ed91109582f11752398"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ySkpyvGQYLQX7dKL7KJ6bwchBIB/GHEM8QJZ6L7N3FI5aD7sjO/Ua554Zfs50YMcmCiINshIeTN8khWRVZhsAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T12:05:24.189650Z","bundle_sha256":"91e662c9e03af19f7954ba38fae9c571ab8020cd3a53d05cfd1e624e3a6c429b"}}