{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JZH5TJTM5N22Z6JLCCTAEQUXUB","short_pith_number":"pith:JZH5TJTM","canonical_record":{"source":{"id":"1903.00041","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T19:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"17c36c49c27636403a0fc9627af45bee4a0d19a37adbebeae128351162e6f95b","abstract_canon_sha256":"ea9a70e579e1f9b495b5b22a9d97008c65557744b992594c930898f1e5391cc8"},"schema_version":"1.0"},"canonical_sha256":"4e4fd9a66ceb75acf92b10a6024297a05cc12068e9ddd14bd7581b92bbf62dd7","source":{"kind":"arxiv","id":"1903.00041","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00041","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00041v1","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00041","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"pith_short_12","alias_value":"JZH5TJTM5N22","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JZH5TJTM5N22Z6JL","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JZH5TJTM","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JZH5TJTM5N22Z6JLCCTAEQUXUB","target":"record","payload":{"canonical_record":{"source":{"id":"1903.00041","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T19:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"17c36c49c27636403a0fc9627af45bee4a0d19a37adbebeae128351162e6f95b","abstract_canon_sha256":"ea9a70e579e1f9b495b5b22a9d97008c65557744b992594c930898f1e5391cc8"},"schema_version":"1.0"},"canonical_sha256":"4e4fd9a66ceb75acf92b10a6024297a05cc12068e9ddd14bd7581b92bbf62dd7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:21.114998Z","signature_b64":"qbzoFqjQUXDtFzQt7Ools3/ZuRnaGutGe2xdNZPTQHFTUmybwKI52MxnDV+4C7+aJ+/5et79IxCQGNQOecIDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e4fd9a66ceb75acf92b10a6024297a05cc12068e9ddd14bd7581b92bbf62dd7","last_reissued_at":"2026-05-17T23:52:21.114345Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:21.114345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.00041","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":"2026-05-17T23:52:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fECgC2OxmYAWIoY28B4YpoQhfII8zblPxZAON1KfUr/cPWiagH/c6c4lSpxi0LeFxpmuYPu6Ahq0lNd7f6ICCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T04:07:04.545432Z"},"content_sha256":"7f0dc4688927f12ce0ea8b7be7d4bbe046c59882164fd63fb20e114442935a99","schema_version":"1.0","event_id":"sha256:7f0dc4688927f12ce0ea8b7be7d4bbe046c59882164fd63fb20e114442935a99"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JZH5TJTM5N22Z6JLCCTAEQUXUB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reinforcement Learning based Curriculum Optimization for Neural Machine Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Colin Cherry, Gaurav Kumar, George Foster, Maxim Krikun","submitted_at":"2019-02-28T19:35:09Z","abstract_excerpt":"We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00041","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":"2026-05-17T23:52:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EA5B4UTG5te8trREI6sMdB67G/1jgp/rOiLmhGqtzs5s2EP/5NsfZ2j74cayK8Ih3rIaN7cesiZzkW7aBE7DAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T04:07:04.546081Z"},"content_sha256":"51ead22bade502496f6f3f9889348f4654ea759891a8ac8319b8daa389e8892a","schema_version":"1.0","event_id":"sha256:51ead22bade502496f6f3f9889348f4654ea759891a8ac8319b8daa389e8892a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/bundle.json","state_url":"https://pith.science/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/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":"2026-06-11T04:07:04Z","links":{"resolver":"https://pith.science/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB","bundle":"https://pith.science/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/bundle.json","state":"https://pith.science/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JZH5TJTM5N22Z6JLCCTAEQUXUB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JZH5TJTM5N22Z6JLCCTAEQUXUB","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":"ea9a70e579e1f9b495b5b22a9d97008c65557744b992594c930898f1e5391cc8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T19:35:09Z","title_canon_sha256":"17c36c49c27636403a0fc9627af45bee4a0d19a37adbebeae128351162e6f95b"},"schema_version":"1.0","source":{"id":"1903.00041","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00041","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00041v1","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00041","created_at":"2026-05-17T23:52:21Z"},{"alias_kind":"pith_short_12","alias_value":"JZH5TJTM5N22","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"JZH5TJTM5N22Z6JL","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"JZH5TJTM","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:51ead22bade502496f6f3f9889348f4654ea759891a8ac8319b8daa389e8892a","target":"graph","created_at":"2026-05-17T23:52:21Z","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":"We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, i","authors_text":"Colin Cherry, Gaurav Kumar, George Foster, Maxim Krikun","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T19:35:09Z","title":"Reinforcement Learning based Curriculum Optimization for Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00041","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":"sha256:7f0dc4688927f12ce0ea8b7be7d4bbe046c59882164fd63fb20e114442935a99","target":"record","created_at":"2026-05-17T23:52:21Z","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":"ea9a70e579e1f9b495b5b22a9d97008c65557744b992594c930898f1e5391cc8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T19:35:09Z","title_canon_sha256":"17c36c49c27636403a0fc9627af45bee4a0d19a37adbebeae128351162e6f95b"},"schema_version":"1.0","source":{"id":"1903.00041","kind":"arxiv","version":1}},"canonical_sha256":"4e4fd9a66ceb75acf92b10a6024297a05cc12068e9ddd14bd7581b92bbf62dd7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e4fd9a66ceb75acf92b10a6024297a05cc12068e9ddd14bd7581b92bbf62dd7","first_computed_at":"2026-05-17T23:52:21.114345Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:21.114345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qbzoFqjQUXDtFzQt7Ools3/ZuRnaGutGe2xdNZPTQHFTUmybwKI52MxnDV+4C7+aJ+/5et79IxCQGNQOecIDDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:21.114998Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00041","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f0dc4688927f12ce0ea8b7be7d4bbe046c59882164fd63fb20e114442935a99","sha256:51ead22bade502496f6f3f9889348f4654ea759891a8ac8319b8daa389e8892a"],"state_sha256":"4575a56dba04b0dfd4b6bd90c88e1db1141053f15685b939be30d4634cc17d63"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MYqJZEXfTluQKWmIEp+AMrDU55aedFBwOtS5YzwqsnczQVEKFQRyZeTKVIsmtTEPJSz0qQNqZwOqe4oddTEgBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T04:07:04.549636Z","bundle_sha256":"c0c0af023a3662dedd62dde807df6fcb0570c1acce13297988ce3d91f500ad7e"}}