{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:VIBU23MD34UCZMCLWSAAAHDEBT","short_pith_number":"pith:VIBU23MD","canonical_record":{"source":{"id":"2210.14169","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T17:01:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8e7c990d6860a2f3db5ea349398d83eeb87cd88d42273cf2528927f1b61eb7a2","abstract_canon_sha256":"26e677546e5d7dfc9be6e3f428efccd35cc57cb953e843b06308f548e320f181"},"schema_version":"1.0"},"canonical_sha256":"aa034d6d83df282cb04bb480001c640cc4695e00e1db56747536a682885e00bc","source":{"kind":"arxiv","id":"2210.14169","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14169","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14169v3","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14169","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_12","alias_value":"VIBU23MD34UC","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_16","alias_value":"VIBU23MD34UCZMCL","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_8","alias_value":"VIBU23MD","created_at":"2026-07-05T05:12:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:VIBU23MD34UCZMCLWSAAAHDEBT","target":"record","payload":{"canonical_record":{"source":{"id":"2210.14169","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T17:01:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8e7c990d6860a2f3db5ea349398d83eeb87cd88d42273cf2528927f1b61eb7a2","abstract_canon_sha256":"26e677546e5d7dfc9be6e3f428efccd35cc57cb953e843b06308f548e320f181"},"schema_version":"1.0"},"canonical_sha256":"aa034d6d83df282cb04bb480001c640cc4695e00e1db56747536a682885e00bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:31.821018Z","signature_b64":"FBT3VmfNUdctm5Gxwds3H3jOKCyYv1bnrWR4JFLvADuOMI0mEByQhjjFTIdFqGUqgjsPj0gPuSlNdW3FuwbUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa034d6d83df282cb04bb480001c640cc4695e00e1db56747536a682885e00bc","last_reissued_at":"2026-07-05T05:12:31.820520Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:31.820520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.14169","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T+NH8Dg/5qQd1eNcTqJifmQyJFUuxNE1NIwABq4Cq80wiZnn7pZ1fPQE6LNrQadCdv7YC3QImoP+VL7FGvXWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:26:21.751717Z"},"content_sha256":"d1483506e70bc338f4cabd1891e3f3a5e60161b2cf350896aa0539cf2da9c052","schema_version":"1.0","event_id":"sha256:d1483506e70bc338f4cabd1891e3f3a5e60161b2cf350896aa0539cf2da9c052"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:VIBU23MD34UCZMCLWSAAAHDEBT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Alexandros Papangelis, Andy Rosenbaum, Chenyang Tao, Dilek Hakkani-Tur, Maximillian Chen, Seokhwan Kim, Yang Liu, Zhou Yu","submitted_at":"2022-10-25T17:01:30Z","abstract_excerpt":"Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. Models fine-tuned on our augmented"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14169","kind":"arxiv","version":3},"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/2210.14169/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"06cDuMbsp4c8psGfhjP8FiJoqPIrmcNrgU8JeC6R48c09mjBNsvwL1dM36DZ9F2DIFB3TOwmGMw4pT45ToDMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:26:21.752093Z"},"content_sha256":"1006fc0a1946714ebe1ad6383f36df244d76539eec23734e62422009c546987e","schema_version":"1.0","event_id":"sha256:1006fc0a1946714ebe1ad6383f36df244d76539eec23734e62422009c546987e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VIBU23MD34UCZMCLWSAAAHDEBT/bundle.json","state_url":"https://pith.science/pith/VIBU23MD34UCZMCLWSAAAHDEBT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VIBU23MD34UCZMCLWSAAAHDEBT/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":"2026-07-06T17:26:21Z","links":{"resolver":"https://pith.science/pith/VIBU23MD34UCZMCLWSAAAHDEBT","bundle":"https://pith.science/pith/VIBU23MD34UCZMCLWSAAAHDEBT/bundle.json","state":"https://pith.science/pith/VIBU23MD34UCZMCLWSAAAHDEBT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VIBU23MD34UCZMCLWSAAAHDEBT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VIBU23MD34UCZMCLWSAAAHDEBT","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":"26e677546e5d7dfc9be6e3f428efccd35cc57cb953e843b06308f548e320f181","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T17:01:30Z","title_canon_sha256":"8e7c990d6860a2f3db5ea349398d83eeb87cd88d42273cf2528927f1b61eb7a2"},"schema_version":"1.0","source":{"id":"2210.14169","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.14169","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"arxiv_version","alias_value":"2210.14169v3","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14169","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_12","alias_value":"VIBU23MD34UC","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_16","alias_value":"VIBU23MD34UCZMCL","created_at":"2026-07-05T05:12:31Z"},{"alias_kind":"pith_short_8","alias_value":"VIBU23MD","created_at":"2026-07-05T05:12:31Z"}],"graph_snapshots":[{"event_id":"sha256:1006fc0a1946714ebe1ad6383f36df244d76539eec23734e62422009c546987e","target":"graph","created_at":"2026-07-05T05:12:31Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.14169/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. Models fine-tuned on our augmented","authors_text":"Alexandros Papangelis, Andy Rosenbaum, Chenyang Tao, Dilek Hakkani-Tur, Maximillian Chen, Seokhwan Kim, Yang Liu, Zhou Yu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T17:01:30Z","title":"Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14169","kind":"arxiv","version":3},"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:d1483506e70bc338f4cabd1891e3f3a5e60161b2cf350896aa0539cf2da9c052","target":"record","created_at":"2026-07-05T05:12:31Z","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":"26e677546e5d7dfc9be6e3f428efccd35cc57cb953e843b06308f548e320f181","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T17:01:30Z","title_canon_sha256":"8e7c990d6860a2f3db5ea349398d83eeb87cd88d42273cf2528927f1b61eb7a2"},"schema_version":"1.0","source":{"id":"2210.14169","kind":"arxiv","version":3}},"canonical_sha256":"aa034d6d83df282cb04bb480001c640cc4695e00e1db56747536a682885e00bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa034d6d83df282cb04bb480001c640cc4695e00e1db56747536a682885e00bc","first_computed_at":"2026-07-05T05:12:31.820520Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:31.820520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FBT3VmfNUdctm5Gxwds3H3jOKCyYv1bnrWR4JFLvADuOMI0mEByQhjjFTIdFqGUqgjsPj0gPuSlNdW3FuwbUAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:31.821018Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.14169","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1483506e70bc338f4cabd1891e3f3a5e60161b2cf350896aa0539cf2da9c052","sha256:1006fc0a1946714ebe1ad6383f36df244d76539eec23734e62422009c546987e"],"state_sha256":"2b6256e0c42164c9ceca4216d0c6731fbaa15d9092849e1a2b96d48edd84a205"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7zEfZdjoXzRUX1kwRMSsq9qT0aZkb2khzhA23aqQ2ST3diU53Vla91vzodD9ngJB7CQNJY8du/qojoFV8x+fAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:26:21.754205Z","bundle_sha256":"6c490aff4f32194c27d059d5fae0f7d5723496e3b5277a0068f5edb9743c7136"}}