{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TBJNHAKGPDIJ6OUTY7WYXVZ5BH","short_pith_number":"pith:TBJNHAKG","schema_version":"1.0","canonical_sha256":"9852d3814678d09f3a93c7ed8bd73d09f63c8d02f3fa466edceabc5b42fd4cef","source":{"kind":"arxiv","id":"1901.02222","version":1},"attestation_state":"computed","paper":{"title":"Multi-turn Inference Matching Network for Natural Language Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chunhua Liu, Dong Yu, Hainan Yu, Shan Jiang","submitted_at":"2019-01-08T09:48:41Z","abstract_excerpt":"Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different match"},"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":"1901.02222","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-01-08T09:48:41Z","cross_cats_sorted":[],"title_canon_sha256":"d7158cf67fbcafb58d126c66c1b3a44a0dd4f0a6a23e73a8bc7389103beba685","abstract_canon_sha256":"3b66434e10e726ed7e2b96da7ac36572e9b910ac52130983d6043d037cdb4133"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:43.738977Z","signature_b64":"34ElVDRJKnPpoMGfuzdg6JWmnxlqlz7sWDiNey3qeJ/do8M6zS8BxXnN3N94PvaMI7gjZBVJwtPF71MC9wTkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9852d3814678d09f3a93c7ed8bd73d09f63c8d02f3fa466edceabc5b42fd4cef","last_reissued_at":"2026-05-17T23:56:43.738591Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:43.738591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-turn Inference Matching Network for Natural Language Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chunhua Liu, Dong Yu, Hainan Yu, Shan Jiang","submitted_at":"2019-01-08T09:48:41Z","abstract_excerpt":"Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different match"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02222","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":"1901.02222","created_at":"2026-05-17T23:56:43.738648+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.02222v1","created_at":"2026-05-17T23:56:43.738648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02222","created_at":"2026-05-17T23:56:43.738648+00:00"},{"alias_kind":"pith_short_12","alias_value":"TBJNHAKGPDIJ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"TBJNHAKGPDIJ6OUT","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"TBJNHAKG","created_at":"2026-05-18T12:33:27.125529+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/TBJNHAKGPDIJ6OUTY7WYXVZ5BH","json":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH.json","graph_json":"https://pith.science/api/pith-number/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/graph.json","events_json":"https://pith.science/api/pith-number/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/events.json","paper":"https://pith.science/paper/TBJNHAKG"},"agent_actions":{"view_html":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH","download_json":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH.json","view_paper":"https://pith.science/paper/TBJNHAKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.02222&json=true","fetch_graph":"https://pith.science/api/pith-number/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/graph.json","fetch_events":"https://pith.science/api/pith-number/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/action/storage_attestation","attest_author":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/action/author_attestation","sign_citation":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/action/citation_signature","submit_replication":"https://pith.science/pith/TBJNHAKGPDIJ6OUTY7WYXVZ5BH/action/replication_record"}},"created_at":"2026-05-17T23:56:43.738648+00:00","updated_at":"2026-05-17T23:56:43.738648+00:00"}