{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QX2I4UXSJ5AXONB2YCUPSENCC2","short_pith_number":"pith:QX2I4UXS","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"},"canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","source":{"kind":"arxiv","id":"1809.00013","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00013","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00013v1","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00013","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"pith_short_12","alias_value":"QX2I4UXSJ5AX","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"QX2I4UXSJ5AXONB2","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"QX2I4UXS","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QX2I4UXSJ5AXONB2YCUPSENCC2","target":"record","payload":{"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"},"canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","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"},"source_kind":"arxiv","source_id":"1809.00013","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":"2026-05-18T00:06:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CA0ypZAlqd/p+DrU7tuku46XJCpP4A912mWG7cPtKZN5HT9sADbRGQmoNUsMIkBW+OQ1WMy/j19GUxtyd7SWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T09:33:18.379845Z"},"content_sha256":"42e29b59d9e33d7cf33461c1977ed9575f2f710db98f3ae594f8b8f38734475b","schema_version":"1.0","event_id":"sha256:42e29b59d9e33d7cf33461c1977ed9575f2f710db98f3ae594f8b8f38734475b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QX2I4UXSJ5AXONB2YCUPSENCC2","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kYrW4++Kvy1YkbaIYzF8JMsxujqItQhAM17Dqjj82/pjDudXWKy9ZrulAYyb42o1AXxAyjLoVbOoreaYSHWUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T09:33:18.380494Z"},"content_sha256":"a32741a85b7e0cca8552191f1cf95581839645b79fea6856701db2d666904311","schema_version":"1.0","event_id":"sha256:a32741a85b7e0cca8552191f1cf95581839645b79fea6856701db2d666904311"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/bundle.json","state_url":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/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":"2026-06-06T09:33:18Z","links":{"resolver":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2","bundle":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/bundle.json","state":"https://pith.science/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QX2I4UXSJ5AXONB2YCUPSENCC2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QX2I4UXSJ5AXONB2YCUPSENCC2","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":"59a0b4b8a9fef865d2d5a6e16b147655d29ee49b1144c67356405feab755859d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-31T18:00:27Z","title_canon_sha256":"aed32b6b0c040c6b5de000d66e76f7eb15a7af8740e53a75eea729eef9e6e708"},"schema_version":"1.0","source":{"id":"1809.00013","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00013","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00013v1","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00013","created_at":"2026-05-18T00:06:36Z"},{"alias_kind":"pith_short_12","alias_value":"QX2I4UXSJ5AX","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"QX2I4UXSJ5AXONB2","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"QX2I4UXS","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:a32741a85b7e0cca8552191f1cf95581839645b79fea6856701db2d666904311","target":"graph","created_at":"2026-05-18T00:06:36Z","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":"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 ","authors_text":"David Alvarez-Melis, Tommi S. Jaakkola","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-31T18:00:27Z","title":"Gromov-Wasserstein Alignment of Word Embedding Spaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00013","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":"sha256:42e29b59d9e33d7cf33461c1977ed9575f2f710db98f3ae594f8b8f38734475b","target":"record","created_at":"2026-05-18T00:06:36Z","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":"59a0b4b8a9fef865d2d5a6e16b147655d29ee49b1144c67356405feab755859d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-31T18:00:27Z","title_canon_sha256":"aed32b6b0c040c6b5de000d66e76f7eb15a7af8740e53a75eea729eef9e6e708"},"schema_version":"1.0","source":{"id":"1809.00013","kind":"arxiv","version":1}},"canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85f48e52f24f4177343ac0a8f911a216b1e7811d87031055eff860c0dc391987","first_computed_at":"2026-05-18T00:06:36.393707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:36.393707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K8taDciwkONTaeUMfIdy/U7QGD2/GsSy+vhXKZa+2tLK6ed7yqaKfXX47oOUunaAUmswpjy3QY/DhKCjHSiODQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:36.394278Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00013","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:42e29b59d9e33d7cf33461c1977ed9575f2f710db98f3ae594f8b8f38734475b","sha256:a32741a85b7e0cca8552191f1cf95581839645b79fea6856701db2d666904311"],"state_sha256":"43090feb74f390713e35539e6157464c7a317b3df94486e8e7d85e539d5ce7a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6rzgdBCe6JAWAz7G4hG9Nq18zbGnth12C2q5Vpl64/6YTaCaaJqogaUyoS84G4QEY9ljgqTSTLOUbrOKM5XCCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T09:33:18.384452Z","bundle_sha256":"73534bdc50c41c6326f884206cb7884dd4d314dc79ad6aa608e55d561562223f"}}