{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7UM5TMZZHRVG3MIDSPGG342JT7","short_pith_number":"pith:7UM5TMZZ","canonical_record":{"source":{"id":"2212.02745","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-06T04:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"f144b936ded4f4ee2ca7d6fad94ee74825abd4c217f9ea5070e87e8605c66348","abstract_canon_sha256":"33490ee81797ddc4b80f49ee18e507fe64ee6a47bc7a1b8d257672daecdc5ef5"},"schema_version":"1.0"},"canonical_sha256":"fd19d9b3393c6a6db10393cc6df3499ff118bce67bbb39ecf416b9e355de3bfb","source":{"kind":"arxiv","id":"2212.02745","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.02745","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"arxiv_version","alias_value":"2212.02745v2","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02745","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_12","alias_value":"7UM5TMZZHRVG","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_16","alias_value":"7UM5TMZZHRVG3MID","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_8","alias_value":"7UM5TMZZ","created_at":"2026-07-05T06:35:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7UM5TMZZHRVG3MIDSPGG342JT7","target":"record","payload":{"canonical_record":{"source":{"id":"2212.02745","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-06T04:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"f144b936ded4f4ee2ca7d6fad94ee74825abd4c217f9ea5070e87e8605c66348","abstract_canon_sha256":"33490ee81797ddc4b80f49ee18e507fe64ee6a47bc7a1b8d257672daecdc5ef5"},"schema_version":"1.0"},"canonical_sha256":"fd19d9b3393c6a6db10393cc6df3499ff118bce67bbb39ecf416b9e355de3bfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:46.748130Z","signature_b64":"fRg48DOiWQSCeOHM4w2PTVsHwe7rfn2yDNcheU6hm3fLm1AS5o1UvEqkoRhW5RinkM9ZEA+57aVjYCC1oxlNCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd19d9b3393c6a6db10393cc6df3499ff118bce67bbb39ecf416b9e355de3bfb","last_reissued_at":"2026-07-05T06:35:46.747714Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:46.747714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.02745","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:35:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UomsRK3VcHx2EO8m1KTR5wcxZikplhIZu9tlfVOVt48gXSfI7vs9gODzSBwjCB32TCShMDJKAeAeHulp9uxuCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T06:35:59.856037Z"},"content_sha256":"b0faf0500ed3dfb46d50a453f89066e9fe6235892d5481163f8ca0ac3030caa9","schema_version":"1.0","event_id":"sha256:b0faf0500ed3dfb46d50a453f89066e9fe6235892d5481163f8ca0ac3030caa9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7UM5TMZZHRVG3MIDSPGG342JT7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sources of Noise in Dialogue and How to Deal with Them","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Derek Chen, Zhou Yu","submitted_at":"2022-12-06T04:36:32Z","abstract_excerpt":"Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems. In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise. Our results reveal that models are quite robust to label errors commonly tackled "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02745","kind":"arxiv","version":2},"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/2212.02745/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":"2026-07-05T06:35:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VUEW08EH4D5jQ1LXjxvzYmxQ6oW3wkKeM1Nkr3GLb4+zA205Gcx+yRDVGcrcpp6tfXAd7BYCYHOeN4dwyc3/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T06:35:59.856428Z"},"content_sha256":"31af634178d89f9e430fb76ab940af7f111c1d195f134d88d70590c79c4c299d","schema_version":"1.0","event_id":"sha256:31af634178d89f9e430fb76ab940af7f111c1d195f134d88d70590c79c4c299d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7UM5TMZZHRVG3MIDSPGG342JT7/bundle.json","state_url":"https://pith.science/pith/7UM5TMZZHRVG3MIDSPGG342JT7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7UM5TMZZHRVG3MIDSPGG342JT7/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":"2026-07-25T06:35:59Z","links":{"resolver":"https://pith.science/pith/7UM5TMZZHRVG3MIDSPGG342JT7","bundle":"https://pith.science/pith/7UM5TMZZHRVG3MIDSPGG342JT7/bundle.json","state":"https://pith.science/pith/7UM5TMZZHRVG3MIDSPGG342JT7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7UM5TMZZHRVG3MIDSPGG342JT7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7UM5TMZZHRVG3MIDSPGG342JT7","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":"33490ee81797ddc4b80f49ee18e507fe64ee6a47bc7a1b8d257672daecdc5ef5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-06T04:36:32Z","title_canon_sha256":"f144b936ded4f4ee2ca7d6fad94ee74825abd4c217f9ea5070e87e8605c66348"},"schema_version":"1.0","source":{"id":"2212.02745","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.02745","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"arxiv_version","alias_value":"2212.02745v2","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02745","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_12","alias_value":"7UM5TMZZHRVG","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_16","alias_value":"7UM5TMZZHRVG3MID","created_at":"2026-07-05T06:35:46Z"},{"alias_kind":"pith_short_8","alias_value":"7UM5TMZZ","created_at":"2026-07-05T06:35:46Z"}],"graph_snapshots":[{"event_id":"sha256:31af634178d89f9e430fb76ab940af7f111c1d195f134d88d70590c79c4c299d","target":"graph","created_at":"2026-07-05T06:35:46Z","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/2212.02745/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems. In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise. Our results reveal that models are quite robust to label errors commonly tackled ","authors_text":"Derek Chen, Zhou Yu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-06T04:36:32Z","title":"Sources of Noise in Dialogue and How to Deal with Them"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02745","kind":"arxiv","version":2},"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:b0faf0500ed3dfb46d50a453f89066e9fe6235892d5481163f8ca0ac3030caa9","target":"record","created_at":"2026-07-05T06:35:46Z","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":"33490ee81797ddc4b80f49ee18e507fe64ee6a47bc7a1b8d257672daecdc5ef5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-06T04:36:32Z","title_canon_sha256":"f144b936ded4f4ee2ca7d6fad94ee74825abd4c217f9ea5070e87e8605c66348"},"schema_version":"1.0","source":{"id":"2212.02745","kind":"arxiv","version":2}},"canonical_sha256":"fd19d9b3393c6a6db10393cc6df3499ff118bce67bbb39ecf416b9e355de3bfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fd19d9b3393c6a6db10393cc6df3499ff118bce67bbb39ecf416b9e355de3bfb","first_computed_at":"2026-07-05T06:35:46.747714Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:35:46.747714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fRg48DOiWQSCeOHM4w2PTVsHwe7rfn2yDNcheU6hm3fLm1AS5o1UvEqkoRhW5RinkM9ZEA+57aVjYCC1oxlNCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:35:46.748130Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.02745","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0faf0500ed3dfb46d50a453f89066e9fe6235892d5481163f8ca0ac3030caa9","sha256:31af634178d89f9e430fb76ab940af7f111c1d195f134d88d70590c79c4c299d"],"state_sha256":"b4c2258950c25db8d06adfa5cec6d2ace80fb3dd6ec575784351bfcf37dbbf2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8oxuitCmWX8GnQJOUFY8u1GAuoU+aO4HYq0sZzPkUTOnSL4OsQIEIiCtmiurMEwGosG3/yvY22GNkZ3hViwrBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T06:35:59.858900Z","bundle_sha256":"314e4f1f676ded92e598abe426922f4423bbfa7003451c5f3f1532d10072d7c9"}}