{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:F53MS6XK6DPO4HQSOP6TH6GK2V","short_pith_number":"pith:F53MS6XK","canonical_record":{"source":{"id":"1706.05565","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-17T17:36:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"68d5079ad2f367f6bd246f1d477fc676d6a5d7efc2a56c41f7ba7d8df7318c52","abstract_canon_sha256":"736897605637254d43af6442d18f1352f4cd0e57bf708390ff627e3197214dbc"},"schema_version":"1.0"},"canonical_sha256":"2f76c97aeaf0deee1e1273fd33f8cad56a4701a1f941975cf628111a13a9e1cc","source":{"kind":"arxiv","id":"1706.05565","version":8},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05565","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05565v8","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05565","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"pith_short_12","alias_value":"F53MS6XK6DPO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F53MS6XK6DPO4HQS","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F53MS6XK","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:F53MS6XK6DPO4HQSOP6TH6GK2V","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05565","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-17T17:36:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"68d5079ad2f367f6bd246f1d477fc676d6a5d7efc2a56c41f7ba7d8df7318c52","abstract_canon_sha256":"736897605637254d43af6442d18f1352f4cd0e57bf708390ff627e3197214dbc"},"schema_version":"1.0"},"canonical_sha256":"2f76c97aeaf0deee1e1273fd33f8cad56a4701a1f941975cf628111a13a9e1cc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:05.660025Z","signature_b64":"Et5b6aOB0jg7pRTqD5mUTi3dQv0akxWs1vt95Qdu/3ZAGE48ya0BMu7y3O5y4RdB/JmICYU4VmO5sGf++vY1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f76c97aeaf0deee1e1273fd33f8cad56a4701a1f941975cf628111a13a9e1cc","last_reissued_at":"2026-05-18T00:05:05.659190Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:05.659190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05565","source_version":8,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:05:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d4WmDde9D2GP76c2YsQrVdCFheCdcoLVwvthaoIKtxR8fJ2aKGRZFkBvSblghzmGzVlW17fJEE/mljeJ+noKCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:13:46.490971Z"},"content_sha256":"b6dc44cb8b5d5672c901625adc6384e1d366320704a70f48da89f65e4adc5bf2","schema_version":"1.0","event_id":"sha256:b6dc44cb8b5d5672c901625adc6384e1d366320704a70f48da89f65e4adc5bf2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:F53MS6XK6DPO4HQSOP6TH6GK2V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Neural Phrase-based Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CL","authors_text":"Chong Wang, Dengyong Zhou, Li Deng, Po-Sen Huang, Sitao Huang","submitted_at":"2017-06-17T17:36:23Z","abstract_excerpt":"In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05565","kind":"arxiv","version":8},"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":"2026-05-18T00:05:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"97/JZ99AmQxAgnbjWnS4FGD1ASWCqq/4mjcMJU3fuv8eT6mJfDvdmCkeidBU2VLWaMy7Rc0IHsg+QHs3NaPSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T13:13:46.491586Z"},"content_sha256":"28cd26c30dd473f709e25ed1e0cf79a81f4d0332039718d156150edc9b939356","schema_version":"1.0","event_id":"sha256:28cd26c30dd473f709e25ed1e0cf79a81f4d0332039718d156150edc9b939356"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/bundle.json","state_url":"https://pith.science/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/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":"2026-05-26T13:13:46Z","links":{"resolver":"https://pith.science/pith/F53MS6XK6DPO4HQSOP6TH6GK2V","bundle":"https://pith.science/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/bundle.json","state":"https://pith.science/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F53MS6XK6DPO4HQSOP6TH6GK2V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:F53MS6XK6DPO4HQSOP6TH6GK2V","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":"736897605637254d43af6442d18f1352f4cd0e57bf708390ff627e3197214dbc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-17T17:36:23Z","title_canon_sha256":"68d5079ad2f367f6bd246f1d477fc676d6a5d7efc2a56c41f7ba7d8df7318c52"},"schema_version":"1.0","source":{"id":"1706.05565","kind":"arxiv","version":8}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05565","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05565v8","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05565","created_at":"2026-05-18T00:05:05Z"},{"alias_kind":"pith_short_12","alias_value":"F53MS6XK6DPO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F53MS6XK6DPO4HQS","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F53MS6XK","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:28cd26c30dd473f709e25ed1e0cf79a81f4d0332039718d156150edc9b939356","target":"graph","created_at":"2026-05-18T00:05:05Z","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":"In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our e","authors_text":"Chong Wang, Dengyong Zhou, Li Deng, Po-Sen Huang, Sitao Huang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-17T17:36:23Z","title":"Towards Neural Phrase-based Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05565","kind":"arxiv","version":8},"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:b6dc44cb8b5d5672c901625adc6384e1d366320704a70f48da89f65e4adc5bf2","target":"record","created_at":"2026-05-18T00:05:05Z","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":"736897605637254d43af6442d18f1352f4cd0e57bf708390ff627e3197214dbc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-17T17:36:23Z","title_canon_sha256":"68d5079ad2f367f6bd246f1d477fc676d6a5d7efc2a56c41f7ba7d8df7318c52"},"schema_version":"1.0","source":{"id":"1706.05565","kind":"arxiv","version":8}},"canonical_sha256":"2f76c97aeaf0deee1e1273fd33f8cad56a4701a1f941975cf628111a13a9e1cc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f76c97aeaf0deee1e1273fd33f8cad56a4701a1f941975cf628111a13a9e1cc","first_computed_at":"2026-05-18T00:05:05.659190Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:05.659190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Et5b6aOB0jg7pRTqD5mUTi3dQv0akxWs1vt95Qdu/3ZAGE48ya0BMu7y3O5y4RdB/JmICYU4VmO5sGf++vY1Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:05.660025Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05565","source_kind":"arxiv","source_version":8}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6dc44cb8b5d5672c901625adc6384e1d366320704a70f48da89f65e4adc5bf2","sha256:28cd26c30dd473f709e25ed1e0cf79a81f4d0332039718d156150edc9b939356"],"state_sha256":"4c26f5872616b92bc5a1688cc505ded96c59187dc3229561922b0de18edd85fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/mMgBrBQzUcqSjKfkvLIkqh/06tYXIoQV5qhx6tovudp/CWgDN3q3PIpfuz8Z4sBaS7C67r6WefV8UIlOMefAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T13:13:46.495337Z","bundle_sha256":"54492f236f00c9b881587e7f954df0e7ffa72c7a96c95ba36397fbdd11bb8ac8"}}