{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:RLXEQCHHQ7KSS6DMLFK772O3SU","short_pith_number":"pith:RLXEQCHH","canonical_record":{"source":{"id":"1907.06170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-14T05:47:53Z","cross_cats_sorted":[],"title_canon_sha256":"49401e9c51a023ca4a7c7a88ff6487f629b4bdee0c344b87c3cb7075a49040a7","abstract_canon_sha256":"d621ffe4632d474ad089ddd9321c3197395189ef83d8442a99144c025d87f43e"},"schema_version":"1.0"},"canonical_sha256":"8aee4808e787d529786c5955ffe9db951be72f7422a39ed608bebf4b2eb8f79c","source":{"kind":"arxiv","id":"1907.06170","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06170","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06170v1","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06170","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"pith_short_12","alias_value":"RLXEQCHHQ7KS","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RLXEQCHHQ7KSS6DM","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RLXEQCHH","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:RLXEQCHHQ7KSS6DMLFK772O3SU","target":"record","payload":{"canonical_record":{"source":{"id":"1907.06170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-14T05:47:53Z","cross_cats_sorted":[],"title_canon_sha256":"49401e9c51a023ca4a7c7a88ff6487f629b4bdee0c344b87c3cb7075a49040a7","abstract_canon_sha256":"d621ffe4632d474ad089ddd9321c3197395189ef83d8442a99144c025d87f43e"},"schema_version":"1.0"},"canonical_sha256":"8aee4808e787d529786c5955ffe9db951be72f7422a39ed608bebf4b2eb8f79c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:38.319294Z","signature_b64":"4l4GbLcpk2kaYiulYmi56r1lycytJyyboSMN7GKYDBzNCaP8ysh+GUOQPRa7I8bfb+jTPKcr5Z4TYy0zkjgWCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8aee4808e787d529786c5955ffe9db951be72f7422a39ed608bebf4b2eb8f79c","last_reissued_at":"2026-05-17T23:40:38.318724Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:38.318724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.06170","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":"2026-05-17T23:40:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SGdoLb8gNtP9uZfhayUi1h95b+bhAmx0k5nfk0PU1oBEUvqFkQmgpAwSh+asplhqMpiVAYf/0yyIS1WpWHjNDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:52:08.715213Z"},"content_sha256":"0b2879be40d7fc46d7d03b594168acd7a802357f1a1ef60dfa30369a1b8ea6c0","schema_version":"1.0","event_id":"sha256:0b2879be40d7fc46d7d03b594168acd7a802357f1a1ef60dfa30369a1b8ea6c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:RLXEQCHHQ7KSS6DMLFK772O3SU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Marcin Junczys-Dowmunt","submitted_at":"2019-07-14T05:47:53Z","abstract_excerpt":"This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06170","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":"2026-05-17T23:40:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wAOgxYAuRe2p2bUfELWYrYzaxmI+w5Xg8+HtHZcvq/e2z6WK+NdbXX8Swxu1EScKX9I0GJkYzxMAVvdLBV2QDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T21:52:08.715568Z"},"content_sha256":"72c16037f07bad4e321b0498b1410fbd593581604d0100acaf9bb4aa3327fb99","schema_version":"1.0","event_id":"sha256:72c16037f07bad4e321b0498b1410fbd593581604d0100acaf9bb4aa3327fb99"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/bundle.json","state_url":"https://pith.science/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/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":"2026-06-08T21:52:08Z","links":{"resolver":"https://pith.science/pith/RLXEQCHHQ7KSS6DMLFK772O3SU","bundle":"https://pith.science/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/bundle.json","state":"https://pith.science/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RLXEQCHHQ7KSS6DMLFK772O3SU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RLXEQCHHQ7KSS6DMLFK772O3SU","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":"d621ffe4632d474ad089ddd9321c3197395189ef83d8442a99144c025d87f43e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-14T05:47:53Z","title_canon_sha256":"49401e9c51a023ca4a7c7a88ff6487f629b4bdee0c344b87c3cb7075a49040a7"},"schema_version":"1.0","source":{"id":"1907.06170","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06170","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06170v1","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06170","created_at":"2026-05-17T23:40:38Z"},{"alias_kind":"pith_short_12","alias_value":"RLXEQCHHQ7KS","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RLXEQCHHQ7KSS6DM","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RLXEQCHH","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:72c16037f07bad4e321b0498b1410fbd593581604d0100acaf9bb4aa3327fb99","target":"graph","created_at":"2026-05-17T23:40:38Z","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":"This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parall","authors_text":"Marcin Junczys-Dowmunt","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-14T05:47:53Z","title":"Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06170","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":"sha256:0b2879be40d7fc46d7d03b594168acd7a802357f1a1ef60dfa30369a1b8ea6c0","target":"record","created_at":"2026-05-17T23:40:38Z","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":"d621ffe4632d474ad089ddd9321c3197395189ef83d8442a99144c025d87f43e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-14T05:47:53Z","title_canon_sha256":"49401e9c51a023ca4a7c7a88ff6487f629b4bdee0c344b87c3cb7075a49040a7"},"schema_version":"1.0","source":{"id":"1907.06170","kind":"arxiv","version":1}},"canonical_sha256":"8aee4808e787d529786c5955ffe9db951be72f7422a39ed608bebf4b2eb8f79c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8aee4808e787d529786c5955ffe9db951be72f7422a39ed608bebf4b2eb8f79c","first_computed_at":"2026-05-17T23:40:38.318724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:38.318724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4l4GbLcpk2kaYiulYmi56r1lycytJyyboSMN7GKYDBzNCaP8ysh+GUOQPRa7I8bfb+jTPKcr5Z4TYy0zkjgWCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:38.319294Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.06170","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b2879be40d7fc46d7d03b594168acd7a802357f1a1ef60dfa30369a1b8ea6c0","sha256:72c16037f07bad4e321b0498b1410fbd593581604d0100acaf9bb4aa3327fb99"],"state_sha256":"6753965fbc30d4b57b3292c19c10ea19a3e742dba339bb698e1f1ca5dfb31b9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XQwEQFsdut/fog7J+NlSMTaXK5Qhew++p/XSIObIlY2D1g37qcGnopSpvrpRUDx0BiPeccPlvBqX+b6cTK4+Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T21:52:08.717449Z","bundle_sha256":"3c73d617c10bd85487987cdafd97f9de3ab0eb15f5fbac261b4098aa8980536f"}}