{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QX2I4UXSJ5AXONB2YCUPSENCC2","short_pith_number":"pith:QX2I4UXS","schema_version":"1.0","canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","source":{"kind":"arxiv","id":"1809.00013","version":1},"attestation_state":"computed","paper":{"title":"Gromov-Wasserstein Alignment of Word Embedding Spaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"David Alvarez-Melis, Tommi S. Jaakkola","submitted_at":"2018-08-31T18:00:27Z","abstract_excerpt":"Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures "},"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":"1809.00013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-31T18:00:27Z","cross_cats_sorted":[],"title_canon_sha256":"aed32b6b0c040c6b5de000d66e76f7eb15a7af8740e53a75eea729eef9e6e708","abstract_canon_sha256":"59a0b4b8a9fef865d2d5a6e16b147655d29ee49b1144c67356405feab755859d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:36.394278Z","signature_b64":"K8taDciwkONTaeUMfIdy/U7QGD2/GsSy+vhXKZa+2tLK6ed7yqaKfXX47oOUunaAUmswpjy3QY/DhKCjHSiODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","last_reissued_at":"2026-05-18T00:06:36.393707Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:36.393707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gromov-Wasserstein Alignment of Word Embedding Spaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"David Alvarez-Melis, Tommi S. Jaakkola","submitted_at":"2018-08-31T18:00:27Z","abstract_excerpt":"Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00013","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":"1809.00013","created_at":"2026-05-18T00:06:36.393783+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.00013v1","created_at":"2026-05-18T00:06:36.393783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00013","created_at":"2026-05-18T00:06:36.393783+00:00"},{"alias_kind":"pith_short_12","alias_value":"QX2I4UXSJ5AX","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"QX2I4UXSJ5AXONB2","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"QX2I4UXS","created_at":"2026-05-18T12:32:50.500415+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/QX2I4UXSJ5AXONB2YCUPSENCC2","json":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2.json","graph_json":"https://pith.science/api/pith-number/QX2I4UXSJ5AXONB2YCUPSENCC2/graph.json","events_json":"https://pith.science/api/pith-number/QX2I4UXSJ5AXONB2YCUPSENCC2/events.json","paper":"https://pith.science/paper/QX2I4UXS"},"agent_actions":{"view_html":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2","download_json":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2.json","view_paper":"https://pith.science/paper/QX2I4UXS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.00013&json=true","fetch_graph":"https://pith.science/api/pith-number/QX2I4UXSJ5AXONB2YCUPSENCC2/graph.json","fetch_events":"https://pith.science/api/pith-number/QX2I4UXSJ5AXONB2YCUPSENCC2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/action/storage_attestation","attest_author":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/action/author_attestation","sign_citation":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/action/citation_signature","submit_replication":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/action/replication_record"}},"created_at":"2026-05-18T00:06:36.393783+00:00","updated_at":"2026-05-18T00:06:36.393783+00:00"}