{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TP7HSJIKQ22MYEF7GERAQ4KCUB","short_pith_number":"pith:TP7HSJIK","canonical_record":{"source":{"id":"1706.04389","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-14T09:59:47Z","cross_cats_sorted":[],"title_canon_sha256":"24b163b6718481686fcd852071492708887e4d6b82b8a2a39f1bd041497916cb","abstract_canon_sha256":"6db712f59a24526aaa95db80dcb20dad4d2c781af28b9dfd50b82bd103eed3f1"},"schema_version":"1.0"},"canonical_sha256":"9bfe79250a86b4cc10bf3122087142a0432277f28feb35476c4d7b0ac65a220e","source":{"kind":"arxiv","id":"1706.04389","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04389","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04389v1","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04389","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"pith_short_12","alias_value":"TP7HSJIKQ22M","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TP7HSJIKQ22MYEF7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TP7HSJIK","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TP7HSJIKQ22MYEF7GERAQ4KCUB","target":"record","payload":{"canonical_record":{"source":{"id":"1706.04389","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-14T09:59:47Z","cross_cats_sorted":[],"title_canon_sha256":"24b163b6718481686fcd852071492708887e4d6b82b8a2a39f1bd041497916cb","abstract_canon_sha256":"6db712f59a24526aaa95db80dcb20dad4d2c781af28b9dfd50b82bd103eed3f1"},"schema_version":"1.0"},"canonical_sha256":"9bfe79250a86b4cc10bf3122087142a0432277f28feb35476c4d7b0ac65a220e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:56.484412Z","signature_b64":"KktlyZ8axg1JG9NcdBJwoQ65sUhQzVdmfEFoVbTpDU5OuTBuSlmO7bvPL59zI506TxsFLLCI48GZpQdkbZYMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bfe79250a86b4cc10bf3122087142a0432277f28feb35476c4d7b0ac65a220e","last_reissued_at":"2026-05-18T00:23:56.483936Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:56.483936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.04389","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":"2026-05-18T00:23:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ktb/6bNjykXSByJSOOB285PGdrPu5BK+oh4ztCfamsEMCgxoyZ7VOgtjLiKiC8q/eiVIl5A9Jjg/m3IV/tJvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:47:28.490531Z"},"content_sha256":"f793b2491ed53d7335591c6ecba9ca03d3539548cf2c16649b77dd8fbdbafea3","schema_version":"1.0","event_id":"sha256:f793b2491ed53d7335591c6ecba9ca03d3539548cf2c16649b77dd8fbdbafea3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TP7HSJIKQ22MYEF7GERAQ4KCUB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-grained human evaluation of neural versus phrase-based machine translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Antonio Toral, Filip Klubi\\v{c}ka, V\\'ictor M. S\\'anchez-Cartagena","submitted_at":"2017-06-14T09:59:47Z","abstract_excerpt":"We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04389","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":"2026-05-18T00:23:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g0OQ/WcS22/ijkBc/Ef+l/dM1l0zUisvu8lS/VlPzHRMydu6L+FugJ+eTeNfkUM7PbCNCNlRLolyAh6rY+PqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:47:28.491191Z"},"content_sha256":"333e60251f3b70536c3ad7f22989f891d612d35f436766dc75dcc685da050856","schema_version":"1.0","event_id":"sha256:333e60251f3b70536c3ad7f22989f891d612d35f436766dc75dcc685da050856"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/bundle.json","state_url":"https://pith.science/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/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":"2026-05-25T15:47:28Z","links":{"resolver":"https://pith.science/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB","bundle":"https://pith.science/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/bundle.json","state":"https://pith.science/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TP7HSJIKQ22MYEF7GERAQ4KCUB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TP7HSJIKQ22MYEF7GERAQ4KCUB","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":"6db712f59a24526aaa95db80dcb20dad4d2c781af28b9dfd50b82bd103eed3f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-14T09:59:47Z","title_canon_sha256":"24b163b6718481686fcd852071492708887e4d6b82b8a2a39f1bd041497916cb"},"schema_version":"1.0","source":{"id":"1706.04389","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04389","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04389v1","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04389","created_at":"2026-05-18T00:23:56Z"},{"alias_kind":"pith_short_12","alias_value":"TP7HSJIKQ22M","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TP7HSJIKQ22MYEF7","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TP7HSJIK","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:333e60251f3b70536c3ad7f22989f891d612d35f436766dc75dcc685da050856","target":"graph","created_at":"2026-05-18T00:23:56Z","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 compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.","authors_text":"Antonio Toral, Filip Klubi\\v{c}ka, V\\'ictor M. S\\'anchez-Cartagena","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-14T09:59:47Z","title":"Fine-grained human evaluation of neural versus phrase-based machine translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04389","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:f793b2491ed53d7335591c6ecba9ca03d3539548cf2c16649b77dd8fbdbafea3","target":"record","created_at":"2026-05-18T00:23:56Z","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":"6db712f59a24526aaa95db80dcb20dad4d2c781af28b9dfd50b82bd103eed3f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-14T09:59:47Z","title_canon_sha256":"24b163b6718481686fcd852071492708887e4d6b82b8a2a39f1bd041497916cb"},"schema_version":"1.0","source":{"id":"1706.04389","kind":"arxiv","version":1}},"canonical_sha256":"9bfe79250a86b4cc10bf3122087142a0432277f28feb35476c4d7b0ac65a220e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9bfe79250a86b4cc10bf3122087142a0432277f28feb35476c4d7b0ac65a220e","first_computed_at":"2026-05-18T00:23:56.483936Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:56.483936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KktlyZ8axg1JG9NcdBJwoQ65sUhQzVdmfEFoVbTpDU5OuTBuSlmO7bvPL59zI506TxsFLLCI48GZpQdkbZYMBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:56.484412Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.04389","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f793b2491ed53d7335591c6ecba9ca03d3539548cf2c16649b77dd8fbdbafea3","sha256:333e60251f3b70536c3ad7f22989f891d612d35f436766dc75dcc685da050856"],"state_sha256":"9f8ae6b2689a028ca1ec5a3bb2b004d69a28979f91b8223a75cb54aba2e525d9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iV6hTpNQZUh8zZ/Y4BhEJrWpCkuauorWnECe1uR6oDqTSsPpQ803cn+YeAmP5y1v4Ue7/8vfD5unA/1XUzjUCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:47:28.495282Z","bundle_sha256":"f3cdbfc4dc994c8c28e7f872eadea719b365cb70ead3d8826cc194151d6ab149"}}