{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LZ2LQRB2HRE7KM7RHBOKEYE2RZ","short_pith_number":"pith:LZ2LQRB2","canonical_record":{"source":{"id":"1905.11471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T19:44:33Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"35716d7124f385a53c522d79e76a2785edd1905db262814a154d14796b13024c","abstract_canon_sha256":"19079161d3707b0c6d77028111963281e77d09f8c82a9b18dfe1606fca4acab5"},"schema_version":"1.0"},"canonical_sha256":"5e74b8443a3c49f533f1385ca2609a8e776771904ebf88f1c036478397e2fa0d","source":{"kind":"arxiv","id":"1905.11471","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11471","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11471v1","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11471","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"pith_short_12","alias_value":"LZ2LQRB2HRE7","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LZ2LQRB2HRE7KM7R","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LZ2LQRB2","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LZ2LQRB2HRE7KM7RHBOKEYE2RZ","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T19:44:33Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"35716d7124f385a53c522d79e76a2785edd1905db262814a154d14796b13024c","abstract_canon_sha256":"19079161d3707b0c6d77028111963281e77d09f8c82a9b18dfe1606fca4acab5"},"schema_version":"1.0"},"canonical_sha256":"5e74b8443a3c49f533f1385ca2609a8e776771904ebf88f1c036478397e2fa0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:53.329796Z","signature_b64":"RxNWhOPS5tJq/3eUV2a84Hvco9DI6cjTWlK6SSZEUr2YOsgkZu2UbUv59uqzPoldkjSYdV6wZqxD6t5ITrT2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e74b8443a3c49f533f1385ca2609a8e776771904ebf88f1c036478397e2fa0d","last_reissued_at":"2026-05-17T23:44:53.329114Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:53.329114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11471","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":"2026-05-17T23:44:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1YDadIFYBEdLCcDpnBptLpc/SfyrsMuLabREvbPS2qsVf8pbfCMCBXP0r4ruM0LfAAYeZvFQjxcUoj7R1R9ODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T13:50:15.554381Z"},"content_sha256":"49baf685088a9ec556c42cd767a45cdf99526b9864a658b62d157d954d256f5d","schema_version":"1.0","event_id":"sha256:49baf685088a9ec556c42cd767a45cdf99526b9864a658b62d157d954d256f5d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LZ2LQRB2HRE7KM7RHBOKEYE2RZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bryan McCann, Caiming Xiong, Jasdeep Singh, Nitish Shirish Keskar, Richard Socher","submitted_at":"2019-05-27T19:44:33Z","abstract_excerpt":"While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11471","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":"2026-05-17T23:44:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g9MBDvZryzXP62intdbgR0eWmF0z41CkdF43OxcCT0b8gLujPcioJkaomQaNN1Mll95BK4nBPnWawTdw7ubLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T13:50:15.555066Z"},"content_sha256":"b81791846c0a6961887776c146f408b82ac184c0287108b26a52bc73f82311b9","schema_version":"1.0","event_id":"sha256:b81791846c0a6961887776c146f408b82ac184c0287108b26a52bc73f82311b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/bundle.json","state_url":"https://pith.science/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/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":"2026-05-27T13:50:15Z","links":{"resolver":"https://pith.science/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ","bundle":"https://pith.science/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/bundle.json","state":"https://pith.science/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LZ2LQRB2HRE7KM7RHBOKEYE2RZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LZ2LQRB2HRE7KM7RHBOKEYE2RZ","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":"19079161d3707b0c6d77028111963281e77d09f8c82a9b18dfe1606fca4acab5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T19:44:33Z","title_canon_sha256":"35716d7124f385a53c522d79e76a2785edd1905db262814a154d14796b13024c"},"schema_version":"1.0","source":{"id":"1905.11471","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11471","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11471v1","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11471","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"pith_short_12","alias_value":"LZ2LQRB2HRE7","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LZ2LQRB2HRE7KM7R","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LZ2LQRB2","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:b81791846c0a6961887776c146f408b82ac184c0287108b26a52bc73f82311b9","target":"graph","created_at":"2026-05-17T23:44:53Z","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":"While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, a","authors_text":"Bryan McCann, Caiming Xiong, Jasdeep Singh, Nitish Shirish Keskar, Richard Socher","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T19:44:33Z","title":"XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11471","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":"sha256:49baf685088a9ec556c42cd767a45cdf99526b9864a658b62d157d954d256f5d","target":"record","created_at":"2026-05-17T23:44:53Z","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":"19079161d3707b0c6d77028111963281e77d09f8c82a9b18dfe1606fca4acab5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-27T19:44:33Z","title_canon_sha256":"35716d7124f385a53c522d79e76a2785edd1905db262814a154d14796b13024c"},"schema_version":"1.0","source":{"id":"1905.11471","kind":"arxiv","version":1}},"canonical_sha256":"5e74b8443a3c49f533f1385ca2609a8e776771904ebf88f1c036478397e2fa0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e74b8443a3c49f533f1385ca2609a8e776771904ebf88f1c036478397e2fa0d","first_computed_at":"2026-05-17T23:44:53.329114Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:53.329114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RxNWhOPS5tJq/3eUV2a84Hvco9DI6cjTWlK6SSZEUr2YOsgkZu2UbUv59uqzPoldkjSYdV6wZqxD6t5ITrT2DA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:53.329796Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11471","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49baf685088a9ec556c42cd767a45cdf99526b9864a658b62d157d954d256f5d","sha256:b81791846c0a6961887776c146f408b82ac184c0287108b26a52bc73f82311b9"],"state_sha256":"16e11292831399c558c3709079c091b40170d3b83adbdb97687b9b18f6a6130b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LQ43UluMevFmOn5KcRIh0dH2dLO/w1m3tuc7wvV5Q1bLkSGSqXrvOh4HHQj8OkRvDL/K5b/Co1vdmxswO4bnDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T13:50:15.558923Z","bundle_sha256":"0034e9b3abab09bdffd911bbff32715419b4fc9710386723faf16fa8282be2cc"}}