{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:7PQQHPDBX3HDHIYBRBSBK7KKEW","short_pith_number":"pith:7PQQHPDB","canonical_record":{"source":{"id":"1908.07688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-21T03:03:12Z","cross_cats_sorted":[],"title_canon_sha256":"c540caa1e8e8ba70455cd618f0ec3c22e63dc7f8e6e4081ce19d93b1ac47c229","abstract_canon_sha256":"d4568070e7390241f6c5cdef567400c3ce2ff042265a1d3af610213b41386171"},"schema_version":"1.0"},"canonical_sha256":"fbe103bc61bece33a3018864157d4a2596eeec519cd92811c618abc905d02985","source":{"kind":"arxiv","id":"1908.07688","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07688","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07688v1","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07688","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"7PQQHPDBX3HD","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"7PQQHPDBX3HDHIYB","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"7PQQHPDB","created_at":"2026-07-04T23:59:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:7PQQHPDBX3HDHIYBRBSBK7KKEW","target":"record","payload":{"canonical_record":{"source":{"id":"1908.07688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-21T03:03:12Z","cross_cats_sorted":[],"title_canon_sha256":"c540caa1e8e8ba70455cd618f0ec3c22e63dc7f8e6e4081ce19d93b1ac47c229","abstract_canon_sha256":"d4568070e7390241f6c5cdef567400c3ce2ff042265a1d3af610213b41386171"},"schema_version":"1.0"},"canonical_sha256":"fbe103bc61bece33a3018864157d4a2596eeec519cd92811c618abc905d02985","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:00.396475Z","signature_b64":"X8GYJl+DT52WrEoLYyI8EwwxyZ8C8FewGrTCGl/t20MN5GQloYtujnpdDqJT4M80z5pPJ8C8oZ2RitxoQPKUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbe103bc61bece33a3018864157d4a2596eeec519cd92811c618abc905d02985","last_reissued_at":"2026-07-04T23:59:00.396093Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:00.396093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.07688","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":"2026-07-04T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UVfxWJGrtY9LvO0GpHHtiMVD9ckZqfp+Ms5j8cZC1B7WJo2OpYZiVhtDkQuCdte+61M6+NOcFR9sXq7SU2j4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:50:57.512654Z"},"content_sha256":"ebc2fbd258d23b0da74b59e75f89988eed63cdf8c1fa1d4b14a4ece374bb37af","schema_version":"1.0","event_id":"sha256:ebc2fbd258d23b0da74b59e75f89988eed63cdf8c1fa1d4b14a4ece374bb37af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:7PQQHPDBX3HDHIYBRBSBK7KKEW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Neural Machine Translation with Pre-trained Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Heng Yu, Jiajun Chen, Rongxiang Weng, Shujian Huang, Weihua Luo","submitted_at":"2019-08-21T03:03:12Z","abstract_excerpt":"Monolingual data has been demonstrated to be helpful in improving the translation quality of neural machine translation (NMT). The current methods stay at the usage of word-level knowledge, such as generating synthetic parallel data or extracting information from word embedding. In contrast, the power of sentence-level contextual knowledge which is more complex and diverse, playing an important role in natural language generation, has not been fully exploited. In this paper, we propose a novel structure which could leverage monolingual data to acquire sentence-level contextual representations."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07688","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/1908.07688/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":"2026-07-04T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lmji/6fLndppQLLzLeEGP+woNkSr7RFOlf3wdZ+ta5mVtz/F8hR+Yo1angFHNQEXvMauDcgPKLAsLRAOhjHVDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:50:57.513151Z"},"content_sha256":"c1721b7fe1d70d278ea582a834ca00f451dcd31b57d78d37be6ec9eaf79888cc","schema_version":"1.0","event_id":"sha256:c1721b7fe1d70d278ea582a834ca00f451dcd31b57d78d37be6ec9eaf79888cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/bundle.json","state_url":"https://pith.science/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/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":"2026-08-10T23:50:57Z","links":{"resolver":"https://pith.science/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW","bundle":"https://pith.science/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/bundle.json","state":"https://pith.science/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7PQQHPDBX3HDHIYBRBSBK7KKEW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7PQQHPDBX3HDHIYBRBSBK7KKEW","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":"d4568070e7390241f6c5cdef567400c3ce2ff042265a1d3af610213b41386171","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-21T03:03:12Z","title_canon_sha256":"c540caa1e8e8ba70455cd618f0ec3c22e63dc7f8e6e4081ce19d93b1ac47c229"},"schema_version":"1.0","source":{"id":"1908.07688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07688","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07688v1","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07688","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"7PQQHPDBX3HD","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"7PQQHPDBX3HDHIYB","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"7PQQHPDB","created_at":"2026-07-04T23:59:00Z"}],"graph_snapshots":[{"event_id":"sha256:c1721b7fe1d70d278ea582a834ca00f451dcd31b57d78d37be6ec9eaf79888cc","target":"graph","created_at":"2026-07-04T23:59:00Z","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/1908.07688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Monolingual data has been demonstrated to be helpful in improving the translation quality of neural machine translation (NMT). The current methods stay at the usage of word-level knowledge, such as generating synthetic parallel data or extracting information from word embedding. In contrast, the power of sentence-level contextual knowledge which is more complex and diverse, playing an important role in natural language generation, has not been fully exploited. In this paper, we propose a novel structure which could leverage monolingual data to acquire sentence-level contextual representations.","authors_text":"Heng Yu, Jiajun Chen, Rongxiang Weng, Shujian Huang, Weihua Luo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-21T03:03:12Z","title":"Improving Neural Machine Translation with Pre-trained Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07688","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:ebc2fbd258d23b0da74b59e75f89988eed63cdf8c1fa1d4b14a4ece374bb37af","target":"record","created_at":"2026-07-04T23:59:00Z","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":"d4568070e7390241f6c5cdef567400c3ce2ff042265a1d3af610213b41386171","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-21T03:03:12Z","title_canon_sha256":"c540caa1e8e8ba70455cd618f0ec3c22e63dc7f8e6e4081ce19d93b1ac47c229"},"schema_version":"1.0","source":{"id":"1908.07688","kind":"arxiv","version":1}},"canonical_sha256":"fbe103bc61bece33a3018864157d4a2596eeec519cd92811c618abc905d02985","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbe103bc61bece33a3018864157d4a2596eeec519cd92811c618abc905d02985","first_computed_at":"2026-07-04T23:59:00.396093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:00.396093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X8GYJl+DT52WrEoLYyI8EwwxyZ8C8FewGrTCGl/t20MN5GQloYtujnpdDqJT4M80z5pPJ8C8oZ2RitxoQPKUAg==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:00.396475Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.07688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ebc2fbd258d23b0da74b59e75f89988eed63cdf8c1fa1d4b14a4ece374bb37af","sha256:c1721b7fe1d70d278ea582a834ca00f451dcd31b57d78d37be6ec9eaf79888cc"],"state_sha256":"0b41f5463ca97a3f271046e211d78630ad2bf0e3449900caccac293c1699f0a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kRbJpe8HMJYaC2MVavDVr9trFj6T/DI3vZBvtoRmkByjaPiDx7QIC0fxCDd34GKr6TXvQqIAArKgaJbs2GgkCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T23:50:57.516687Z","bundle_sha256":"44a3723368af70f505766aa0a8d727a3c624f102a3cfb843ecc1c6ddbdd07f18"}}