{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:CU7OZJ6727PVNBX6HTWN2AYMWE","short_pith_number":"pith:CU7OZJ67","schema_version":"1.0","canonical_sha256":"153eeca7dfd7df5686fe3cecdd030cb116c0b709c608ca3d2e737193859bba00","source":{"kind":"arxiv","id":"1410.2082","version":2},"attestation_state":"computed","paper":{"title":"Contrastive Unsupervised Word Alignment with Non-Local Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Maosong Sun, Yang Liu","submitted_at":"2014-10-08T12:24:38Z","abstract_excerpt":"Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not on"},"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":"1410.2082","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-10-08T12:24:38Z","cross_cats_sorted":[],"title_canon_sha256":"f188723df16a4027de41c89fa7eff5b292722b8a22043801013ebf661b612073","abstract_canon_sha256":"e6f6a237ee339c004972d1aa7633a8e64e26c8152c06cab2d2605dad42f890d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:37.930576Z","signature_b64":"N1kpW6FjP5OvDCQAZjKCEokDcfJ7mFIlOfhTIwEnbrvKbNkgRGVHMHwbzKeMWaUJOvapvh5PLHTV3x2pBU70BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"153eeca7dfd7df5686fe3cecdd030cb116c0b709c608ca3d2e737193859bba00","last_reissued_at":"2026-05-18T02:40:37.929806Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:37.929806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contrastive Unsupervised Word Alignment with Non-Local Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Maosong Sun, Yang Liu","submitted_at":"2014-10-08T12:24:38Z","abstract_excerpt":"Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.2082","kind":"arxiv","version":2},"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":"1410.2082","created_at":"2026-05-18T02:40:37.929942+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.2082v2","created_at":"2026-05-18T02:40:37.929942+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.2082","created_at":"2026-05-18T02:40:37.929942+00:00"},{"alias_kind":"pith_short_12","alias_value":"CU7OZJ6727PV","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_16","alias_value":"CU7OZJ6727PVNBX6","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_8","alias_value":"CU7OZJ67","created_at":"2026-05-18T12:28:25.294606+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/CU7OZJ6727PVNBX6HTWN2AYMWE","json":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE.json","graph_json":"https://pith.science/api/pith-number/CU7OZJ6727PVNBX6HTWN2AYMWE/graph.json","events_json":"https://pith.science/api/pith-number/CU7OZJ6727PVNBX6HTWN2AYMWE/events.json","paper":"https://pith.science/paper/CU7OZJ67"},"agent_actions":{"view_html":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE","download_json":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE.json","view_paper":"https://pith.science/paper/CU7OZJ67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.2082&json=true","fetch_graph":"https://pith.science/api/pith-number/CU7OZJ6727PVNBX6HTWN2AYMWE/graph.json","fetch_events":"https://pith.science/api/pith-number/CU7OZJ6727PVNBX6HTWN2AYMWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE/action/storage_attestation","attest_author":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE/action/author_attestation","sign_citation":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE/action/citation_signature","submit_replication":"https://pith.science/pith/CU7OZJ6727PVNBX6HTWN2AYMWE/action/replication_record"}},"created_at":"2026-05-18T02:40:37.929942+00:00","updated_at":"2026-05-18T02:40:37.929942+00:00"}