{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YPPB5PK5IN4LIEKKX2C5YRI56Z","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":"b1d3097e3349c04d4bb1b29552504e48a4e9f4a2b2beb8e9dd9ea27a5c3fc7d7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T21:56:22Z","title_canon_sha256":"130dfbf3b3989213de49ee0196c49bbe194dfa9020a1a6e385b8012ed5ff5187"},"schema_version":"1.0","source":{"id":"2410.14057","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.14057","created_at":"2026-07-05T09:22:26Z"},{"alias_kind":"arxiv_version","alias_value":"2410.14057v1","created_at":"2026-07-05T09:22:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14057","created_at":"2026-07-05T09:22:26Z"},{"alias_kind":"pith_short_12","alias_value":"YPPB5PK5IN4L","created_at":"2026-07-05T09:22:26Z"},{"alias_kind":"pith_short_16","alias_value":"YPPB5PK5IN4LIEKK","created_at":"2026-07-05T09:22:26Z"},{"alias_kind":"pith_short_8","alias_value":"YPPB5PK5","created_at":"2026-07-05T09:22:26Z"}],"graph_snapshots":[{"event_id":"sha256:ec05c63a00e7a517f23ad2a3edf3ff08631ced9d65c724471e9b834420157628","target":"graph","created_at":"2026-07-05T09:22:26Z","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/2410.14057/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In this paper, we address the problem of cross-cultural translation on two fronts: (i) we introduce XC-Translate, the first large-scale, manually-created benchmark for machine translation that focuses on text that contains potentially culturally-nuanced entity names, and (ii) we propose KG-MT, a novel end-to-end m","authors_text":"Daniel Lee, Min Li, Saloni Potdar, Simone Conia, Umar Farooq Minhas, Yunyao Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T21:56:22Z","title":"Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14057","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:e0c10461003e919825fc47fa7e92ef5936cc4e1ab76fb90c7149c5adde2ca0d3","target":"record","created_at":"2026-07-05T09:22:26Z","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":"b1d3097e3349c04d4bb1b29552504e48a4e9f4a2b2beb8e9dd9ea27a5c3fc7d7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T21:56:22Z","title_canon_sha256":"130dfbf3b3989213de49ee0196c49bbe194dfa9020a1a6e385b8012ed5ff5187"},"schema_version":"1.0","source":{"id":"2410.14057","kind":"arxiv","version":1}},"canonical_sha256":"c3de1ebd5d4378b4114abe85dc451df673799389e69e1deb58bb28beaa7bd0fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3de1ebd5d4378b4114abe85dc451df673799389e69e1deb58bb28beaa7bd0fa","first_computed_at":"2026-07-05T09:22:26.527927Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:22:26.527927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UWSdyOryaptpEI2R95Bpxkg180MktFsntSPAAXuhi+0u09rfRmD1QaX791+ANd4ypLejMfQj33PiUDWaU+jMAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:22:26.528415Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.14057","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0c10461003e919825fc47fa7e92ef5936cc4e1ab76fb90c7149c5adde2ca0d3","sha256:ec05c63a00e7a517f23ad2a3edf3ff08631ced9d65c724471e9b834420157628"],"state_sha256":"1fd53b8b907f15a4cc5c66e7748e717b994e1b1b7af96112745368051593af39"}