{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MBRUT2F3M3EBPX2CVP3WC2QBIL","short_pith_number":"pith:MBRUT2F3","schema_version":"1.0","canonical_sha256":"606349e8bb66c817df42abf7616a0142c1ffff84f1dd83ed77b8c1bdad9c2470","source":{"kind":"arxiv","id":"1906.08942","version":1},"attestation_state":"computed","paper":{"title":"Be Consistent! Improving Procedural Text Comprehension using Label Consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Antoine Bosselut, Bhavana Dalvi Mishra, Claire Cardie, Niket Tandon, Peter Clark, Wen-tau Yih, Xinya Du","submitted_at":"2019-06-21T04:29:22Z","abstract_excerpt":"Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent advances, current systems still struggle with this task. Our approach is to leverage the fact that, for many procedural texts, multiple independent descriptions are readily available, and that predictions from them should be consistent (label consistency). We present a new learning framework that levera"},"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":"1906.08942","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-21T04:29:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4b8f02f15a64073deabf1f76e1f5588d767b3adce8ec94d8aa8843ff97528fc2","abstract_canon_sha256":"353fcdb74604d5b48c4caa81572e5c591b7885a84908b4ad9eb9f56043d65390"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:47.330623Z","signature_b64":"YEk6HOrRgkLASe3ouP5I2mhsJrjGcu4a8zsLbzko6PDzc5CRiZfAt/QME6AsPOeyq5a/uqO8mT22n51TYlK5DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"606349e8bb66c817df42abf7616a0142c1ffff84f1dd83ed77b8c1bdad9c2470","last_reissued_at":"2026-05-17T23:42:47.330093Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:47.330093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Be Consistent! Improving Procedural Text Comprehension using Label Consistency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Antoine Bosselut, Bhavana Dalvi Mishra, Claire Cardie, Niket Tandon, Peter Clark, Wen-tau Yih, Xinya Du","submitted_at":"2019-06-21T04:29:22Z","abstract_excerpt":"Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent advances, current systems still struggle with this task. Our approach is to leverage the fact that, for many procedural texts, multiple independent descriptions are readily available, and that predictions from them should be consistent (label consistency). We present a new learning framework that levera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08942","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":"1906.08942","created_at":"2026-05-17T23:42:47.330166+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.08942v1","created_at":"2026-05-17T23:42:47.330166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08942","created_at":"2026-05-17T23:42:47.330166+00:00"},{"alias_kind":"pith_short_12","alias_value":"MBRUT2F3M3EB","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"MBRUT2F3M3EBPX2C","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"MBRUT2F3","created_at":"2026-05-18T12:33:21.387695+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/MBRUT2F3M3EBPX2CVP3WC2QBIL","json":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL.json","graph_json":"https://pith.science/api/pith-number/MBRUT2F3M3EBPX2CVP3WC2QBIL/graph.json","events_json":"https://pith.science/api/pith-number/MBRUT2F3M3EBPX2CVP3WC2QBIL/events.json","paper":"https://pith.science/paper/MBRUT2F3"},"agent_actions":{"view_html":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL","download_json":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL.json","view_paper":"https://pith.science/paper/MBRUT2F3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.08942&json=true","fetch_graph":"https://pith.science/api/pith-number/MBRUT2F3M3EBPX2CVP3WC2QBIL/graph.json","fetch_events":"https://pith.science/api/pith-number/MBRUT2F3M3EBPX2CVP3WC2QBIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL/action/storage_attestation","attest_author":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL/action/author_attestation","sign_citation":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL/action/citation_signature","submit_replication":"https://pith.science/pith/MBRUT2F3M3EBPX2CVP3WC2QBIL/action/replication_record"}},"created_at":"2026-05-17T23:42:47.330166+00:00","updated_at":"2026-05-17T23:42:47.330166+00:00"}