{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZXD34N6C2XRNBUZQTCEP22Q4YB","short_pith_number":"pith:ZXD34N6C","canonical_record":{"source":{"id":"1707.07469","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-24T10:35:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1fe8a7f476cbeb7cfdadfcfc458ada4b89b4f628310609836cf2d715e14f5e16","abstract_canon_sha256":"99e486e1a04860304dd49fec12e3702d6135468ab991258036010fe515198fd7"},"schema_version":"1.0"},"canonical_sha256":"cdc7be37c2d5e2d0d3309888fd6a1cc07a9296363a6fa46c8414f834e2c6d0c5","source":{"kind":"arxiv","id":"1707.07469","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07469","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07469v1","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07469","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"pith_short_12","alias_value":"ZXD34N6C2XRN","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZXD34N6C2XRNBUZQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZXD34N6C","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZXD34N6C2XRNBUZQTCEP22Q4YB","target":"record","payload":{"canonical_record":{"source":{"id":"1707.07469","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-24T10:35:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1fe8a7f476cbeb7cfdadfcfc458ada4b89b4f628310609836cf2d715e14f5e16","abstract_canon_sha256":"99e486e1a04860304dd49fec12e3702d6135468ab991258036010fe515198fd7"},"schema_version":"1.0"},"canonical_sha256":"cdc7be37c2d5e2d0d3309888fd6a1cc07a9296363a6fa46c8414f834e2c6d0c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:42.107396Z","signature_b64":"/sogpwLBXMqpt/NVGsvN9W+4HPucAfbkhTjktVOgerbf5Btv11dqGx8eq2QHgU6bRnIk3rr7ZcNNS7D9txYRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdc7be37c2d5e2d0d3309888fd6a1cc07a9296363a6fa46c8414f834e2c6d0c5","last_reissued_at":"2026-05-18T00:39:42.106687Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:42.106687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.07469","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":"2026-05-18T00:39:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RZdyjZTquNIvhpKp6E2KqnUOhoELC0SotEf9SC4jROzbhdFQMzh5Z8Vtss8cmBX4CAnV5CNrDPX/O3j0K1czDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T22:35:08.642093Z"},"content_sha256":"8444920d80132e86a5ece222fa9c64d49021eff58484692ab348031cdaeed5dc","schema_version":"1.0","event_id":"sha256:8444920d80132e86a5ece222fa9c64d49021eff58484692ab348031cdaeed5dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZXD34N6C2XRNBUZQTCEP22Q4YB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Character-level Intra Attention Network for Natural Language Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Han Yang, Jos\\'e A. R. Fonollosa, Marta R. Costa-juss\\`a","submitted_at":"2017-07-24T10:35:46Z","abstract_excerpt":"Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently.\n  In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07469","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":"2026-05-18T00:39:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QMvuFP4shR7swqSu79bz9CmRxtAaPiQkOPg9QIdc/MrOJ6ThAvYnLRad43f+Xa2F1ZBr2INXweF9hzVx6t2QBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T22:35:08.642761Z"},"content_sha256":"8a24be4f2f8b3aa4b9ad997f14c6be21b8d62b34474255731107c5db9f995e38","schema_version":"1.0","event_id":"sha256:8a24be4f2f8b3aa4b9ad997f14c6be21b8d62b34474255731107c5db9f995e38"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/bundle.json","state_url":"https://pith.science/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/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":"2026-05-20T22:35:08Z","links":{"resolver":"https://pith.science/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB","bundle":"https://pith.science/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/bundle.json","state":"https://pith.science/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZXD34N6C2XRNBUZQTCEP22Q4YB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZXD34N6C2XRNBUZQTCEP22Q4YB","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":"99e486e1a04860304dd49fec12e3702d6135468ab991258036010fe515198fd7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-24T10:35:46Z","title_canon_sha256":"1fe8a7f476cbeb7cfdadfcfc458ada4b89b4f628310609836cf2d715e14f5e16"},"schema_version":"1.0","source":{"id":"1707.07469","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07469","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07469v1","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07469","created_at":"2026-05-18T00:39:42Z"},{"alias_kind":"pith_short_12","alias_value":"ZXD34N6C2XRN","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZXD34N6C2XRNBUZQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZXD34N6C","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:8a24be4f2f8b3aa4b9ad997f14c6be21b8d62b34474255731107c5db9f995e38","target":"graph","created_at":"2026-05-18T00:39:42Z","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":"Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently.\n  In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.","authors_text":"Han Yang, Jos\\'e A. R. Fonollosa, Marta R. Costa-juss\\`a","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-24T10:35:46Z","title":"Character-level Intra Attention Network for Natural Language Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07469","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":"sha256:8444920d80132e86a5ece222fa9c64d49021eff58484692ab348031cdaeed5dc","target":"record","created_at":"2026-05-18T00:39:42Z","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":"99e486e1a04860304dd49fec12e3702d6135468ab991258036010fe515198fd7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-24T10:35:46Z","title_canon_sha256":"1fe8a7f476cbeb7cfdadfcfc458ada4b89b4f628310609836cf2d715e14f5e16"},"schema_version":"1.0","source":{"id":"1707.07469","kind":"arxiv","version":1}},"canonical_sha256":"cdc7be37c2d5e2d0d3309888fd6a1cc07a9296363a6fa46c8414f834e2c6d0c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdc7be37c2d5e2d0d3309888fd6a1cc07a9296363a6fa46c8414f834e2c6d0c5","first_computed_at":"2026-05-18T00:39:42.106687Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:42.106687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/sogpwLBXMqpt/NVGsvN9W+4HPucAfbkhTjktVOgerbf5Btv11dqGx8eq2QHgU6bRnIk3rr7ZcNNS7D9txYRBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:42.107396Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.07469","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8444920d80132e86a5ece222fa9c64d49021eff58484692ab348031cdaeed5dc","sha256:8a24be4f2f8b3aa4b9ad997f14c6be21b8d62b34474255731107c5db9f995e38"],"state_sha256":"46039b741137134b99a0581c50af0e6bfda2c8182420bca6d71968a3f1837e50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Qh3Cb/UkjMaF/D95GbgNVIpRDdPTExNBFqPQsO1Ihaswcw0mhTF/9DqiatgpjJsa/GKqPwtnc4Ot8T2prCgDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T22:35:08.646540Z","bundle_sha256":"4550804e137effac7638f3d1f1c1173225615c5b01755ef70b76a15d7d88b4c0"}}