{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QHJ6WZMQGVY3ZFVRQTMZTJYZDA","short_pith_number":"pith:QHJ6WZMQ","schema_version":"1.0","canonical_sha256":"81d3eb65903571bc96b184d999a719181722d4bc7e8a570d290188b51272dcdb","source":{"kind":"arxiv","id":"1906.06045","version":1},"attestation_state":"computed","paper":{"title":"Learning to Ask Unanswerable Questions for Machine Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Qin, Furu Wei, Haichao Zhu, Li Dong, Ting Liu, Wenhui Wang","submitted_at":"2019-06-14T06:35:10Z","abstract_excerpt":"Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to-sequence model for unanswerable question generation, which effectively captures the interactions between the question and the paragraph. We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset. "},"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":"1906.06045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-14T06:35:10Z","cross_cats_sorted":[],"title_canon_sha256":"7b5204d97537643db9d1c2a5df8474a84bd4d08ca1e5744841dd388710e39429","abstract_canon_sha256":"0f03edf15d5b616488534f65aa6711fb4bc5717ac28515a22e57f6367e9fd5db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:19.934664Z","signature_b64":"7BXjxVqL2Nu2NyW8PCpiW8ZOrqQve8JctHa4cSkbSP6SZFSDfQlyhKFc6MTwEdu+sgTpZx/NUsLyDFejBhFcDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81d3eb65903571bc96b184d999a719181722d4bc7e8a570d290188b51272dcdb","last_reissued_at":"2026-05-17T23:43:19.933924Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:19.933924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Ask Unanswerable Questions for Machine Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bing Qin, Furu Wei, Haichao Zhu, Li Dong, Ting Liu, Wenhui Wang","submitted_at":"2019-06-14T06:35:10Z","abstract_excerpt":"Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to-sequence model for unanswerable question generation, which effectively captures the interactions between the question and the paragraph. We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06045","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":"1906.06045","created_at":"2026-05-17T23:43:19.934045+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.06045v1","created_at":"2026-05-17T23:43:19.934045+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.06045","created_at":"2026-05-17T23:43:19.934045+00:00"},{"alias_kind":"pith_short_12","alias_value":"QHJ6WZMQGVY3","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QHJ6WZMQGVY3ZFVR","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QHJ6WZMQ","created_at":"2026-05-18T12:33:27.125529+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/QHJ6WZMQGVY3ZFVRQTMZTJYZDA","json":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA.json","graph_json":"https://pith.science/api/pith-number/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/graph.json","events_json":"https://pith.science/api/pith-number/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/events.json","paper":"https://pith.science/paper/QHJ6WZMQ"},"agent_actions":{"view_html":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA","download_json":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA.json","view_paper":"https://pith.science/paper/QHJ6WZMQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.06045&json=true","fetch_graph":"https://pith.science/api/pith-number/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/graph.json","fetch_events":"https://pith.science/api/pith-number/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/action/storage_attestation","attest_author":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/action/author_attestation","sign_citation":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/action/citation_signature","submit_replication":"https://pith.science/pith/QHJ6WZMQGVY3ZFVRQTMZTJYZDA/action/replication_record"}},"created_at":"2026-05-17T23:43:19.934045+00:00","updated_at":"2026-05-17T23:43:19.934045+00:00"}