{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:56KMIKLTYGDV2FGJ6X6D7ICCXM","short_pith_number":"pith:56KMIKLT","canonical_record":{"source":{"id":"1811.02076","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-05T23:03:02Z","cross_cats_sorted":[],"title_canon_sha256":"b63f4903901566195c803493ef3f587b2962079dcfef31fdc69265ea718b6c79","abstract_canon_sha256":"2fa5530e21f946ac6cbaf323b8051c49b2ed0bd02d02fb6e85c4798b9c2a1bf2"},"schema_version":"1.0"},"canonical_sha256":"ef94c42973c1875d14c9f5fc3fa042bb07c79aba22a11caddd00dce316ed0632","source":{"kind":"arxiv","id":"1811.02076","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02076","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02076v1","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02076","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"56KMIKLTYGDV","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"56KMIKLTYGDV2FGJ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"56KMIKLT","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:56KMIKLTYGDV2FGJ6X6D7ICCXM","target":"record","payload":{"canonical_record":{"source":{"id":"1811.02076","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-05T23:03:02Z","cross_cats_sorted":[],"title_canon_sha256":"b63f4903901566195c803493ef3f587b2962079dcfef31fdc69265ea718b6c79","abstract_canon_sha256":"2fa5530e21f946ac6cbaf323b8051c49b2ed0bd02d02fb6e85c4798b9c2a1bf2"},"schema_version":"1.0"},"canonical_sha256":"ef94c42973c1875d14c9f5fc3fa042bb07c79aba22a11caddd00dce316ed0632","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:25.099550Z","signature_b64":"z3qprNJOno7sG6xaL8M5CdJ069BKs7bjU7rinJeetqP3LMQ7kmWCkn80uAFLUKyW8hH5DhvZvP/nu06IDeqUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef94c42973c1875d14c9f5fc3fa042bb07c79aba22a11caddd00dce316ed0632","last_reissued_at":"2026-05-18T00:01:25.099161Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:25.099161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.02076","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Ho0b/2z5/QdT1H6PHZja2exzJ4fY9DNmrrDkoOpRTFuINdqYuZydXF3URMFr7KU2dxe0v1ntoL8aCfakOjoBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:56:41.504779Z"},"content_sha256":"43d95e63da1043c53d22a24146d666670cf2d50235383273186e0113ca10ab78","schema_version":"1.0","event_id":"sha256:43d95e63da1043c53d22a24146d666670cf2d50235383273186e0113ca10ab78"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:56KMIKLTYGDV2FGJ6X6D7ICCXM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Span-based Question Answering Systems with Coarsely Labeled Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ankur Parikh, Hao Cheng, Kenton Lee, Kristina Toutanova, Michael Collins, Ming-Wei Chang","submitted_at":"2018-11-05T23:03:02Z","abstract_excerpt":"We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains. Experiments demonstrate that the standard multi-task learning approach of sharing representations is not the most effective way to leverage coarse-grained annotations. Instead, we can explicitly model the latent fine-grained short answer variables and optimize the marginal log-likelihood directly or use a newly proposed \\emph{posterior distillation} learning ob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02076","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Nzg184sPLHuCvEa+W7mBQr3cAZzvj2zzan+W7u5aeoJr9/biAUWQOCPwUh4Kk45mNcJjteDzuDvMbfRUPQGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:56:41.505143Z"},"content_sha256":"e1d9ac16bc283844327138c04057be5a51c621538214763523208cf58ffe592f","schema_version":"1.0","event_id":"sha256:e1d9ac16bc283844327138c04057be5a51c621538214763523208cf58ffe592f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/bundle.json","state_url":"https://pith.science/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T15:56:41Z","links":{"resolver":"https://pith.science/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM","bundle":"https://pith.science/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/bundle.json","state":"https://pith.science/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/56KMIKLTYGDV2FGJ6X6D7ICCXM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:56KMIKLTYGDV2FGJ6X6D7ICCXM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2fa5530e21f946ac6cbaf323b8051c49b2ed0bd02d02fb6e85c4798b9c2a1bf2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-05T23:03:02Z","title_canon_sha256":"b63f4903901566195c803493ef3f587b2962079dcfef31fdc69265ea718b6c79"},"schema_version":"1.0","source":{"id":"1811.02076","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02076","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02076v1","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02076","created_at":"2026-05-18T00:01:25Z"},{"alias_kind":"pith_short_12","alias_value":"56KMIKLTYGDV","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"56KMIKLTYGDV2FGJ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"56KMIKLT","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:e1d9ac16bc283844327138c04057be5a51c621538214763523208cf58ffe592f","target":"graph","created_at":"2026-05-18T00:01:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains. Experiments demonstrate that the standard multi-task learning approach of sharing representations is not the most effective way to leverage coarse-grained annotations. Instead, we can explicitly model the latent fine-grained short answer variables and optimize the marginal log-likelihood directly or use a newly proposed \\emph{posterior distillation} learning ob","authors_text":"Ankur Parikh, Hao Cheng, Kenton Lee, Kristina Toutanova, Michael Collins, Ming-Wei Chang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-05T23:03:02Z","title":"Improving Span-based Question Answering Systems with Coarsely Labeled Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02076","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:43d95e63da1043c53d22a24146d666670cf2d50235383273186e0113ca10ab78","target":"record","created_at":"2026-05-18T00:01:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2fa5530e21f946ac6cbaf323b8051c49b2ed0bd02d02fb6e85c4798b9c2a1bf2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-05T23:03:02Z","title_canon_sha256":"b63f4903901566195c803493ef3f587b2962079dcfef31fdc69265ea718b6c79"},"schema_version":"1.0","source":{"id":"1811.02076","kind":"arxiv","version":1}},"canonical_sha256":"ef94c42973c1875d14c9f5fc3fa042bb07c79aba22a11caddd00dce316ed0632","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef94c42973c1875d14c9f5fc3fa042bb07c79aba22a11caddd00dce316ed0632","first_computed_at":"2026-05-18T00:01:25.099161Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:25.099161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z3qprNJOno7sG6xaL8M5CdJ069BKs7bjU7rinJeetqP3LMQ7kmWCkn80uAFLUKyW8hH5DhvZvP/nu06IDeqUDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:25.099550Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.02076","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43d95e63da1043c53d22a24146d666670cf2d50235383273186e0113ca10ab78","sha256:e1d9ac16bc283844327138c04057be5a51c621538214763523208cf58ffe592f"],"state_sha256":"b9795be715728e9f9cc004d086ef830a963fa96f4b214d51e8212dda01aee1df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OhLuWNGaarfG578hq930UhqWAYVyMUJvfI4FojGJ7g70bSZrxWbPHbbrphao0/lSVvG1RL1W04lhEbC4FrkeDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T15:56:41.507387Z","bundle_sha256":"34a9921a8bc73122a1464ad222715ad747513118806ec7c4cc02a57afe238d2a"}}