{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YYVDIV74WBCKRCVSUMP5RETTID","short_pith_number":"pith:YYVDIV74","schema_version":"1.0","canonical_sha256":"c62a3457fcb044a88ab2a31fd8927340d66460ef0e419be5f8fbe24670c6d616","source":{"kind":"arxiv","id":"1906.01343","version":1},"attestation_state":"computed","paper":{"title":"A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jihun Choi, Sang-goo Lee, Taeuk Kim","submitted_at":"2019-06-04T11:03:49Z","abstract_excerpt":"We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding--based approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence. We further extend the cross-sentence generating framework to facilitate semi-supervised training. We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences. We demonstrate the effectiveness of"},"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":"1906.01343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-04T11:03:49Z","cross_cats_sorted":[],"title_canon_sha256":"ae65d4e86064b3ce602c8205d10b7d48dc269c6d8a1fca19c32637c90dafa386","abstract_canon_sha256":"1861feadbee0e1934158297ff90701750ceaabfead2ff1b13a899b37b9e9ffc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:16.978136Z","signature_b64":"Co5Ge9XM2+LZBxPh0N4w2F5+6MjxfupT/QLoZW4mqo9jsVlc1TnBUrZZ4/O9USGngCa4cX29My2zBqimFyDODg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c62a3457fcb044a88ab2a31fd8927340d66460ef0e419be5f8fbe24670c6d616","last_reissued_at":"2026-05-17T23:44:16.977459Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:16.977459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jihun Choi, Sang-goo Lee, Taeuk Kim","submitted_at":"2019-06-04T11:03:49Z","abstract_excerpt":"We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding--based approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence. We further extend the cross-sentence generating framework to facilitate semi-supervised training. We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences. We demonstrate the effectiveness of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01343","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":"1906.01343","created_at":"2026-05-17T23:44:16.977563+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01343v1","created_at":"2026-05-17T23:44:16.977563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01343","created_at":"2026-05-17T23:44:16.977563+00:00"},{"alias_kind":"pith_short_12","alias_value":"YYVDIV74WBCK","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YYVDIV74WBCKRCVS","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YYVDIV74","created_at":"2026-05-18T12:33:33.725879+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/YYVDIV74WBCKRCVSUMP5RETTID","json":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID.json","graph_json":"https://pith.science/api/pith-number/YYVDIV74WBCKRCVSUMP5RETTID/graph.json","events_json":"https://pith.science/api/pith-number/YYVDIV74WBCKRCVSUMP5RETTID/events.json","paper":"https://pith.science/paper/YYVDIV74"},"agent_actions":{"view_html":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID","download_json":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID.json","view_paper":"https://pith.science/paper/YYVDIV74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01343&json=true","fetch_graph":"https://pith.science/api/pith-number/YYVDIV74WBCKRCVSUMP5RETTID/graph.json","fetch_events":"https://pith.science/api/pith-number/YYVDIV74WBCKRCVSUMP5RETTID/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID/action/storage_attestation","attest_author":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID/action/author_attestation","sign_citation":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID/action/citation_signature","submit_replication":"https://pith.science/pith/YYVDIV74WBCKRCVSUMP5RETTID/action/replication_record"}},"created_at":"2026-05-17T23:44:16.977563+00:00","updated_at":"2026-05-17T23:44:16.977563+00:00"}