{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RSLLXRPK7SOAQ3RBRZCPP5CGFE","short_pith_number":"pith:RSLLXRPK","schema_version":"1.0","canonical_sha256":"8c96bbc5eafc9c086e218e44f7f446293fbdea12197e4cca4d05334fa7b67750","source":{"kind":"arxiv","id":"1611.03932","version":1},"attestation_state":"computed","paper":{"title":"Training IBM Watson using Automatically Generated Question-Answer Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changwoo Jung, Gyuwan Kim, Jaeyoon Yoo, Jangho Lee, Minseok Kim, Sungroh Yoon","submitted_at":"2016-11-12T01:49:48Z","abstract_excerpt":"IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson's training. Recently, a large-scale dataset of over 30 m"},"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":"1611.03932","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-12T01:49:48Z","cross_cats_sorted":[],"title_canon_sha256":"5ba3c05b90c481e8f0bbc6f9651dc1679a2e4df06ec181e411b2e5b3eb21f267","abstract_canon_sha256":"2050ff03f06f0052c27b39128514081bfe8d98cee545a3c356e87134048b18ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:59:18.046410Z","signature_b64":"SfaWLQZWECqxw/aKtPG5f9Oxa+UPQRziFGIlh8UHmhEQAhfuVAchbYRy8O3H+5tZnZvXzlPhL5ohpffRd3e0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c96bbc5eafc9c086e218e44f7f446293fbdea12197e4cca4d05334fa7b67750","last_reissued_at":"2026-05-18T00:59:18.045620Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:59:18.045620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Training IBM Watson using Automatically Generated Question-Answer Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changwoo Jung, Gyuwan Kim, Jaeyoon Yoo, Jangho Lee, Minseok Kim, Sungroh Yoon","submitted_at":"2016-11-12T01:49:48Z","abstract_excerpt":"IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson's training. Recently, a large-scale dataset of over 30 m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03932","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.03932","created_at":"2026-05-18T00:59:18.045748+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.03932v1","created_at":"2026-05-18T00:59:18.045748+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03932","created_at":"2026-05-18T00:59:18.045748+00:00"},{"alias_kind":"pith_short_12","alias_value":"RSLLXRPK7SOA","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RSLLXRPK7SOAQ3RB","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RSLLXRPK","created_at":"2026-05-18T12:30:41.710351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE","json":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE.json","graph_json":"https://pith.science/api/pith-number/RSLLXRPK7SOAQ3RBRZCPP5CGFE/graph.json","events_json":"https://pith.science/api/pith-number/RSLLXRPK7SOAQ3RBRZCPP5CGFE/events.json","paper":"https://pith.science/paper/RSLLXRPK"},"agent_actions":{"view_html":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE","download_json":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE.json","view_paper":"https://pith.science/paper/RSLLXRPK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.03932&json=true","fetch_graph":"https://pith.science/api/pith-number/RSLLXRPK7SOAQ3RBRZCPP5CGFE/graph.json","fetch_events":"https://pith.science/api/pith-number/RSLLXRPK7SOAQ3RBRZCPP5CGFE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE/action/storage_attestation","attest_author":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE/action/author_attestation","sign_citation":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE/action/citation_signature","submit_replication":"https://pith.science/pith/RSLLXRPK7SOAQ3RBRZCPP5CGFE/action/replication_record"}},"created_at":"2026-05-18T00:59:18.045748+00:00","updated_at":"2026-05-18T00:59:18.045748+00:00"}