{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:QNFHKA3VRSURND62NCQ74E5QUD","short_pith_number":"pith:QNFHKA3V","canonical_record":{"source":{"id":"2004.13005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-24T23:32:13Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"b3e3ffc5f01ceb9ee7926687301c63651105b8260d4e49840fd982bf2c284323","abstract_canon_sha256":"5d32f28a09fefde293905a25b55a56554073ea4b5b1eaba1d1c4e221463f071c"},"schema_version":"1.0"},"canonical_sha256":"834a7503758ca9168fda68a1fe13b0a0ded7cc3c0921c2e2cf004fe273b62693","source":{"kind":"arxiv","id":"2004.13005","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.13005","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"arxiv_version","alias_value":"2004.13005v1","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13005","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_12","alias_value":"QNFHKA3VRSUR","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_16","alias_value":"QNFHKA3VRSURND62","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_8","alias_value":"QNFHKA3V","created_at":"2026-07-05T00:58:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:QNFHKA3VRSURND62NCQ74E5QUD","target":"record","payload":{"canonical_record":{"source":{"id":"2004.13005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-24T23:32:13Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"b3e3ffc5f01ceb9ee7926687301c63651105b8260d4e49840fd982bf2c284323","abstract_canon_sha256":"5d32f28a09fefde293905a25b55a56554073ea4b5b1eaba1d1c4e221463f071c"},"schema_version":"1.0"},"canonical_sha256":"834a7503758ca9168fda68a1fe13b0a0ded7cc3c0921c2e2cf004fe273b62693","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:58:54.678580Z","signature_b64":"NjfQfuaQyCQGidC3/wfK/R0gtyXubBTwOjnHKSou2zXmr0Il3sAixVVR6yBJu1vW6OiCFoxdAKlzadQwAtynCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"834a7503758ca9168fda68a1fe13b0a0ded7cc3c0921c2e2cf004fe273b62693","last_reissued_at":"2026-07-05T00:58:54.678200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:58:54.678200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2004.13005","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":"2026-07-05T00:58:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0xPCuUFIa1QnY3gwtWS9qzswL0BOTmI6PjC13JOenBNWhcT36Alp7cZi70jDd2FQQwrPEIKG63eZ5twZYTDyCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:43:19.137403Z"},"content_sha256":"5e938e4dd36f9551178ce1f17b75e64b40ef7527ed1c59c47b21b8d19ad40fec","schema_version":"1.0","event_id":"sha256:5e938e4dd36f9551178ce1f17b75e64b40ef7527ed1c59c47b21b8d19ad40fec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:QNFHKA3VRSURND62NCQ74E5QUD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cross-lingual Information Retrieval with BERT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Amro El-Jaroudi, Damianos Karakos, Lingjun Zhao, William Hartmann, Zhuolin Jiang","submitted_at":"2020-04-24T23:32:13Z","abstract_excerpt":"Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13005","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2004.13005/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:58:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BhEWd6csSup3ZGYTdsXGNQUaPzyL9fQOetp5JE0CopPwOOaS85JRTQTpjyskZ4SVF8MIwQ1pguM1/2qgBghuBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T16:43:19.137937Z"},"content_sha256":"a80a4ac807300709536e002a8815c29438a270d070a893efd3293ba557e8092a","schema_version":"1.0","event_id":"sha256:a80a4ac807300709536e002a8815c29438a270d070a893efd3293ba557e8092a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QNFHKA3VRSURND62NCQ74E5QUD/bundle.json","state_url":"https://pith.science/pith/QNFHKA3VRSURND62NCQ74E5QUD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QNFHKA3VRSURND62NCQ74E5QUD/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":"2026-08-14T16:43:19Z","links":{"resolver":"https://pith.science/pith/QNFHKA3VRSURND62NCQ74E5QUD","bundle":"https://pith.science/pith/QNFHKA3VRSURND62NCQ74E5QUD/bundle.json","state":"https://pith.science/pith/QNFHKA3VRSURND62NCQ74E5QUD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QNFHKA3VRSURND62NCQ74E5QUD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:QNFHKA3VRSURND62NCQ74E5QUD","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":"5d32f28a09fefde293905a25b55a56554073ea4b5b1eaba1d1c4e221463f071c","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-24T23:32:13Z","title_canon_sha256":"b3e3ffc5f01ceb9ee7926687301c63651105b8260d4e49840fd982bf2c284323"},"schema_version":"1.0","source":{"id":"2004.13005","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.13005","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"arxiv_version","alias_value":"2004.13005v1","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13005","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_12","alias_value":"QNFHKA3VRSUR","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_16","alias_value":"QNFHKA3VRSURND62","created_at":"2026-07-05T00:58:54Z"},{"alias_kind":"pith_short_8","alias_value":"QNFHKA3V","created_at":"2026-07-05T00:58:54Z"}],"graph_snapshots":[{"event_id":"sha256:a80a4ac807300709536e002a8815c29438a270d070a893efd3293ba557e8092a","target":"graph","created_at":"2026-07-05T00:58:54Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2004.13005/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made ","authors_text":"Amro El-Jaroudi, Damianos Karakos, Lingjun Zhao, William Hartmann, Zhuolin Jiang","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-24T23:32:13Z","title":"Cross-lingual Information Retrieval with BERT"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13005","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":"sha256:5e938e4dd36f9551178ce1f17b75e64b40ef7527ed1c59c47b21b8d19ad40fec","target":"record","created_at":"2026-07-05T00:58:54Z","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":"5d32f28a09fefde293905a25b55a56554073ea4b5b1eaba1d1c4e221463f071c","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-04-24T23:32:13Z","title_canon_sha256":"b3e3ffc5f01ceb9ee7926687301c63651105b8260d4e49840fd982bf2c284323"},"schema_version":"1.0","source":{"id":"2004.13005","kind":"arxiv","version":1}},"canonical_sha256":"834a7503758ca9168fda68a1fe13b0a0ded7cc3c0921c2e2cf004fe273b62693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"834a7503758ca9168fda68a1fe13b0a0ded7cc3c0921c2e2cf004fe273b62693","first_computed_at":"2026-07-05T00:58:54.678200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:58:54.678200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NjfQfuaQyCQGidC3/wfK/R0gtyXubBTwOjnHKSou2zXmr0Il3sAixVVR6yBJu1vW6OiCFoxdAKlzadQwAtynCw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:58:54.678580Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.13005","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e938e4dd36f9551178ce1f17b75e64b40ef7527ed1c59c47b21b8d19ad40fec","sha256:a80a4ac807300709536e002a8815c29438a270d070a893efd3293ba557e8092a"],"state_sha256":"8fdc3b4237472f55c2cabbd6befe856eb6a596271e4d7037eab375189c427bbd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WlKcp0pG+7E+5pIVw8PNxuHpG8AIIh8b++huzoTlR+Sf8ovZn/BQZr8cO20N+YtKMGiMLjvpYI4Wc90OYrE7AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T16:43:19.154580Z","bundle_sha256":"80d460c3b08bcc95fd7ff01ffe81d115a95656a94d17f8e7d08435ea75dfc05c"}}