{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:EI2JRWK5LPEXOZIWA3LRUOF2AH","short_pith_number":"pith:EI2JRWK5","schema_version":"1.0","canonical_sha256":"223498d95d5bc977651606d71a38ba01e8b97c7e83b97b05df4de7d50087be78","source":{"kind":"arxiv","id":"2010.08422","version":1},"attestation_state":"computed","paper":{"title":"Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Charlotte Pasqual, Mohamed Challal, Wissam Siblini","submitted_at":"2020-10-16T14:36:38Z","abstract_excerpt":"Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such as Bert have shown on SQuAD the ability to surpass humans for extracting answers in small passages of text, they suffer from their high complexity when faced to a much larger search space. The most common way to tackle this problem is to add a preliminary Information Retrieval step to heavily filter the corpus and only keep the relevant passages. In this paper, we propose a more direct and complementary solution whic"},"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":"2010.08422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T14:36:38Z","cross_cats_sorted":[],"title_canon_sha256":"0bb235e0eca9517cdceb5609c41588c61fe8801657c8b993fb4a2be88e700dfe","abstract_canon_sha256":"ff2a2edc4e1d8744b6dd3e2696e924b4fa5d4d22ceac77d2aa5ef41a139bb957"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:43:34.783730Z","signature_b64":"osOS6L5iTpPXqFwD99SfNQAobyVFphrXy9YHTA20+IQBPSEpn6np/pdDJh2lrojqrljHS2Jzx4uxhX7HhId4Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"223498d95d5bc977651606d71a38ba01e8b97c7e83b97b05df4de7d50087be78","last_reissued_at":"2026-07-05T01:43:34.783274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:43:34.783274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Charlotte Pasqual, Mohamed Challal, Wissam Siblini","submitted_at":"2020-10-16T14:36:38Z","abstract_excerpt":"Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such as Bert have shown on SQuAD the ability to surpass humans for extracting answers in small passages of text, they suffer from their high complexity when faced to a much larger search space. The most common way to tackle this problem is to add a preliminary Information Retrieval step to heavily filter the corpus and only keep the relevant passages. In this paper, we propose a more direct and complementary solution whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08422","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/2010.08422/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.08422","created_at":"2026-07-05T01:43:34.783340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.08422v1","created_at":"2026-07-05T01:43:34.783340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08422","created_at":"2026-07-05T01:43:34.783340+00:00"},{"alias_kind":"pith_short_12","alias_value":"EI2JRWK5LPEX","created_at":"2026-07-05T01:43:34.783340+00:00"},{"alias_kind":"pith_short_16","alias_value":"EI2JRWK5LPEXOZIW","created_at":"2026-07-05T01:43:34.783340+00:00"},{"alias_kind":"pith_short_8","alias_value":"EI2JRWK5","created_at":"2026-07-05T01:43:34.783340+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.10507","citing_title":"Multi-Sample Anti-Aliasing and Constrained Optimization for 3D Gaussian Splatting","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH","json":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH.json","graph_json":"https://pith.science/api/pith-number/EI2JRWK5LPEXOZIWA3LRUOF2AH/graph.json","events_json":"https://pith.science/api/pith-number/EI2JRWK5LPEXOZIWA3LRUOF2AH/events.json","paper":"https://pith.science/paper/EI2JRWK5"},"agent_actions":{"view_html":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH","download_json":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH.json","view_paper":"https://pith.science/paper/EI2JRWK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.08422&json=true","fetch_graph":"https://pith.science/api/pith-number/EI2JRWK5LPEXOZIWA3LRUOF2AH/graph.json","fetch_events":"https://pith.science/api/pith-number/EI2JRWK5LPEXOZIWA3LRUOF2AH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH/action/storage_attestation","attest_author":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH/action/author_attestation","sign_citation":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH/action/citation_signature","submit_replication":"https://pith.science/pith/EI2JRWK5LPEXOZIWA3LRUOF2AH/action/replication_record"}},"created_at":"2026-07-05T01:43:34.783340+00:00","updated_at":"2026-07-05T01:43:34.783340+00:00"}