{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TAKTXC6LGEN5PVSIHZIBTSS3IL","short_pith_number":"pith:TAKTXC6L","schema_version":"1.0","canonical_sha256":"98153b8bcb311bd7d6483e5019ca5b42c3a48862cd49f8cb0e3e7a6ecf385ce3","source":{"kind":"arxiv","id":"1911.08648","version":1},"attestation_state":"computed","paper":{"title":"Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Senlin Luo, Xiaorui Zhou, Yunfang Wu","submitted_at":"2019-11-20T00:48:36Z","abstract_excerpt":"In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn't model the interactions between the article and question, making the generated distractors tend to be too ge"},"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":"1911.08648","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-20T00:48:36Z","cross_cats_sorted":[],"title_canon_sha256":"e5874e7ad0f595210b80361ab1064fe07cada70769308c73f2274e7062cd26ef","abstract_canon_sha256":"9c7980121f476a75ed417b631236044e365c3243a82b9c0e223919ed76857b80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:34.604003Z","signature_b64":"0VxqzLEPLxtEY1CvpYePF5Iq/pl9rthq5Xvgu7dOEZy6yygvJ+3Qf3u824OJWbBM3Ge+29nUnS0ZX/H/z+uTBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98153b8bcb311bd7d6483e5019ca5b42c3a48862cd49f8cb0e3e7a6ecf385ce3","last_reissued_at":"2026-07-05T00:20:34.603026Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:34.603026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Senlin Luo, Xiaorui Zhou, Yunfang Wu","submitted_at":"2019-11-20T00:48:36Z","abstract_excerpt":"In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn't model the interactions between the article and question, making the generated distractors tend to be too ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08648","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/1911.08648/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":"1911.08648","created_at":"2026-07-05T00:20:34.603070+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.08648v1","created_at":"2026-07-05T00:20:34.603070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08648","created_at":"2026-07-05T00:20:34.603070+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAKTXC6LGEN5","created_at":"2026-07-05T00:20:34.603070+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAKTXC6LGEN5PVSI","created_at":"2026-07-05T00:20:34.603070+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAKTXC6L","created_at":"2026-07-05T00:20:34.603070+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.13439","citing_title":"D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL","json":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL.json","graph_json":"https://pith.science/api/pith-number/TAKTXC6LGEN5PVSIHZIBTSS3IL/graph.json","events_json":"https://pith.science/api/pith-number/TAKTXC6LGEN5PVSIHZIBTSS3IL/events.json","paper":"https://pith.science/paper/TAKTXC6L"},"agent_actions":{"view_html":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL","download_json":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL.json","view_paper":"https://pith.science/paper/TAKTXC6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.08648&json=true","fetch_graph":"https://pith.science/api/pith-number/TAKTXC6LGEN5PVSIHZIBTSS3IL/graph.json","fetch_events":"https://pith.science/api/pith-number/TAKTXC6LGEN5PVSIHZIBTSS3IL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL/action/storage_attestation","attest_author":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL/action/author_attestation","sign_citation":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL/action/citation_signature","submit_replication":"https://pith.science/pith/TAKTXC6LGEN5PVSIHZIBTSS3IL/action/replication_record"}},"created_at":"2026-07-05T00:20:34.603070+00:00","updated_at":"2026-07-05T00:20:34.603070+00:00"}