{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H7DMMLBMC2PX54VU3MSKXKNFSU","short_pith_number":"pith:H7DMMLBM","schema_version":"1.0","canonical_sha256":"3fc6c62c2c169f7ef2b4db24aba9a5950af62a6b4527e65c741d6a8e62630033","source":{"kind":"arxiv","id":"1907.03112","version":1},"attestation_state":"computed","paper":{"title":"Best Practices for Learning Domain-Specific Cross-Lingual Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Beata Nyari, Chao Li, Lena Shakurova, Mihai Rotaru","submitted_at":"2019-07-06T10:45:45Z","abstract_excerpt":"Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed di"},"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":"1907.03112","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-06T10:45:45Z","cross_cats_sorted":[],"title_canon_sha256":"281df968693d01e983dd9d9d3f202e017ea1711a50499da97d49a2917807def2","abstract_canon_sha256":"cc84563c8b50c52c5afc7dbade248fde0063152a469e123a8119fcf73cffe61e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:06.964006Z","signature_b64":"j4hRNXbbXHXcy4syOACco8Soj8FN+IgME6wg4NPC3jfroqI1hqHueHSrKlAZhO4u52b5XZk79omMHF8n02dqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fc6c62c2c169f7ef2b4db24aba9a5950af62a6b4527e65c741d6a8e62630033","last_reissued_at":"2026-05-17T23:41:06.963371Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:06.963371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Best Practices for Learning Domain-Specific Cross-Lingual Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Beata Nyari, Chao Li, Lena Shakurova, Mihai Rotaru","submitted_at":"2019-07-06T10:45:45Z","abstract_excerpt":"Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03112","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.03112","created_at":"2026-05-17T23:41:06.963458+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03112v1","created_at":"2026-05-17T23:41:06.963458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03112","created_at":"2026-05-17T23:41:06.963458+00:00"},{"alias_kind":"pith_short_12","alias_value":"H7DMMLBMC2PX","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"H7DMMLBMC2PX54VU","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"H7DMMLBM","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU","json":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU.json","graph_json":"https://pith.science/api/pith-number/H7DMMLBMC2PX54VU3MSKXKNFSU/graph.json","events_json":"https://pith.science/api/pith-number/H7DMMLBMC2PX54VU3MSKXKNFSU/events.json","paper":"https://pith.science/paper/H7DMMLBM"},"agent_actions":{"view_html":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU","download_json":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU.json","view_paper":"https://pith.science/paper/H7DMMLBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03112&json=true","fetch_graph":"https://pith.science/api/pith-number/H7DMMLBMC2PX54VU3MSKXKNFSU/graph.json","fetch_events":"https://pith.science/api/pith-number/H7DMMLBMC2PX54VU3MSKXKNFSU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU/action/storage_attestation","attest_author":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU/action/author_attestation","sign_citation":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU/action/citation_signature","submit_replication":"https://pith.science/pith/H7DMMLBMC2PX54VU3MSKXKNFSU/action/replication_record"}},"created_at":"2026-05-17T23:41:06.963458+00:00","updated_at":"2026-05-17T23:41:06.963458+00:00"}