{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LP4HD2BYFHU3VCBMYWKIQ36KRF","short_pith_number":"pith:LP4HD2BY","schema_version":"1.0","canonical_sha256":"5bf871e83829e9ba882cc594886fca8979af5672bdd1be9ef4e95bcb492c23e4","source":{"kind":"arxiv","id":"2306.05183","version":1},"attestation_state":"computed","paper":{"title":"Improving Long Context Document-Level Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Christian Herold, Hermann Ney","submitted_at":"2023-06-08T13:28:48Z","abstract_excerpt":"Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many works have been published on the topic of document-level NMT, but most restrict the system to only local context, typically including just the one or two preceding sentences as additional information. This might be enough to resolve some ambiguous inputs, but it is probably not sufficient to capture some document-level information like the topic or style of a conversation. When increa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.05183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-08T13:28:48Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"25484f667b8ba64ee7259a8d5baf75545dbc6f8457938779a6c27a48340e09b8","abstract_canon_sha256":"072c53f2b617b8b41811eb0ab64e408d6ec006099ef5254af7c89759a517767e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:53.351890Z","signature_b64":"6LWNx27kFMA/8WS+5mjx6qBMR60y+kuvzFfU4STT2TZaWL1tfyNNiB/KI9j71RHlLro7jIKpU44yDn0i3AAWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bf871e83829e9ba882cc594886fca8979af5672bdd1be9ef4e95bcb492c23e4","last_reissued_at":"2026-07-05T06:18:53.351431Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:53.351431Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Long Context Document-Level Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Christian Herold, Hermann Ney","submitted_at":"2023-06-08T13:28:48Z","abstract_excerpt":"Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many works have been published on the topic of document-level NMT, but most restrict the system to only local context, typically including just the one or two preceding sentences as additional information. This might be enough to resolve some ambiguous inputs, but it is probably not sufficient to capture some document-level information like the topic or style of a conversation. When increa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05183","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/2306.05183/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.05183","created_at":"2026-07-05T06:18:53.351488+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.05183v1","created_at":"2026-07-05T06:18:53.351488+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05183","created_at":"2026-07-05T06:18:53.351488+00:00"},{"alias_kind":"pith_short_12","alias_value":"LP4HD2BYFHU3","created_at":"2026-07-05T06:18:53.351488+00:00"},{"alias_kind":"pith_short_16","alias_value":"LP4HD2BYFHU3VCBM","created_at":"2026-07-05T06:18:53.351488+00:00"},{"alias_kind":"pith_short_8","alias_value":"LP4HD2BY","created_at":"2026-07-05T06:18:53.351488+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02461","citing_title":"XToM: Exploring the Multilingual Theory of Mind for Large Language Models","ref_index":2017,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF","json":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF.json","graph_json":"https://pith.science/api/pith-number/LP4HD2BYFHU3VCBMYWKIQ36KRF/graph.json","events_json":"https://pith.science/api/pith-number/LP4HD2BYFHU3VCBMYWKIQ36KRF/events.json","paper":"https://pith.science/paper/LP4HD2BY"},"agent_actions":{"view_html":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF","download_json":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF.json","view_paper":"https://pith.science/paper/LP4HD2BY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.05183&json=true","fetch_graph":"https://pith.science/api/pith-number/LP4HD2BYFHU3VCBMYWKIQ36KRF/graph.json","fetch_events":"https://pith.science/api/pith-number/LP4HD2BYFHU3VCBMYWKIQ36KRF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF/action/storage_attestation","attest_author":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF/action/author_attestation","sign_citation":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF/action/citation_signature","submit_replication":"https://pith.science/pith/LP4HD2BYFHU3VCBMYWKIQ36KRF/action/replication_record"}},"created_at":"2026-07-05T06:18:53.351488+00:00","updated_at":"2026-07-05T06:18:53.351488+00:00"}