{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QJZIINCKVRJ2QGKVQOJ5U2ZGLA","short_pith_number":"pith:QJZIINCK","canonical_record":{"source":{"id":"2102.12227","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-02-24T11:35:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ebe9d691e6156c0d4255346a657873afed2d95184cdb230564e10be2b591ffa2","abstract_canon_sha256":"62e7fa24dbe019fd39ef5513bd0cdf96bb3d97b6fc44a21f84ee682fc13f16f0"},"schema_version":"1.0"},"canonical_sha256":"827284344aac53a819558393da6b265811e088c438de5a4acffbd8bff742c45c","source":{"kind":"arxiv","id":"2102.12227","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12227","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12227v3","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12227","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_12","alias_value":"QJZIINCKVRJ2","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_16","alias_value":"QJZIINCKVRJ2QGKV","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_8","alias_value":"QJZIINCK","created_at":"2026-07-05T06:14:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QJZIINCKVRJ2QGKVQOJ5U2ZGLA","target":"record","payload":{"canonical_record":{"source":{"id":"2102.12227","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-02-24T11:35:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ebe9d691e6156c0d4255346a657873afed2d95184cdb230564e10be2b591ffa2","abstract_canon_sha256":"62e7fa24dbe019fd39ef5513bd0cdf96bb3d97b6fc44a21f84ee682fc13f16f0"},"schema_version":"1.0"},"canonical_sha256":"827284344aac53a819558393da6b265811e088c438de5a4acffbd8bff742c45c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:02.162932Z","signature_b64":"iuF7jCsQJNxKyF6pvo6vLSQeclTzrlCVwK2KyZ8hZkg6puwzbkDLMyeesdhdqoi50IVqz9XzcjLUSXDlIRIwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"827284344aac53a819558393da6b265811e088c438de5a4acffbd8bff742c45c","last_reissued_at":"2026-07-05T06:14:02.162534Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:02.162534Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.12227","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":"2026-07-05T06:14:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4B15sDmDuDLlRtabg/tSMcagIsC/lTs4596SlgDWZB5FRHUEQ838T/qIly7oeRqlO6AHzlAQqLpj0wHH67k/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T09:33:45.426717Z"},"content_sha256":"b74288d02ea370f5f1fbb90f462e4a4a41c0798e7bfc4d645427a7fb962edb06","schema_version":"1.0","event_id":"sha256:b74288d02ea370f5f1fbb90f462e4a4a41c0798e7bfc4d645427a7fb962edb06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QJZIINCKVRJ2QGKVQOJ5U2ZGLA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Task Attentive Residual Networks for Argument Mining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Andrea Galassi, Marco Lippi, Paolo Torroni","submitted_at":"2021-02-24T11:35:28Z","abstract_excerpt":"We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12227","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/2102.12227/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":"2026-07-05T06:14:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yzuAK3QH4BUjOCAlUo63phWi6wYWyPCf+iFgDV2P30VEmrh45VE+2/dN1pfHiOXfpIpImtMaeScUmUIsiGgPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T09:33:45.427833Z"},"content_sha256":"a901a91567a89673d6b4b2e775f71233f17151cc6a6a6893ebd4cc374e37ca69","schema_version":"1.0","event_id":"sha256:a901a91567a89673d6b4b2e775f71233f17151cc6a6a6893ebd4cc374e37ca69"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/bundle.json","state_url":"https://pith.science/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/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":"2026-08-12T09:33:45Z","links":{"resolver":"https://pith.science/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA","bundle":"https://pith.science/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/bundle.json","state":"https://pith.science/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QJZIINCKVRJ2QGKVQOJ5U2ZGLA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QJZIINCKVRJ2QGKVQOJ5U2ZGLA","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":"62e7fa24dbe019fd39ef5513bd0cdf96bb3d97b6fc44a21f84ee682fc13f16f0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-02-24T11:35:28Z","title_canon_sha256":"ebe9d691e6156c0d4255346a657873afed2d95184cdb230564e10be2b591ffa2"},"schema_version":"1.0","source":{"id":"2102.12227","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.12227","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"arxiv_version","alias_value":"2102.12227v3","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12227","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_12","alias_value":"QJZIINCKVRJ2","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_16","alias_value":"QJZIINCKVRJ2QGKV","created_at":"2026-07-05T06:14:02Z"},{"alias_kind":"pith_short_8","alias_value":"QJZIINCK","created_at":"2026-07-05T06:14:02Z"}],"graph_snapshots":[{"event_id":"sha256:a901a91567a89673d6b4b2e775f71233f17151cc6a6a6893ebd4cc374e37ca69","target":"graph","created_at":"2026-07-05T06:14:02Z","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/2102.12227/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesti","authors_text":"Andrea Galassi, Marco Lippi, Paolo Torroni","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-02-24T11:35:28Z","title":"Multi-Task Attentive Residual Networks for Argument Mining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12227","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:b74288d02ea370f5f1fbb90f462e4a4a41c0798e7bfc4d645427a7fb962edb06","target":"record","created_at":"2026-07-05T06:14:02Z","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":"62e7fa24dbe019fd39ef5513bd0cdf96bb3d97b6fc44a21f84ee682fc13f16f0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-02-24T11:35:28Z","title_canon_sha256":"ebe9d691e6156c0d4255346a657873afed2d95184cdb230564e10be2b591ffa2"},"schema_version":"1.0","source":{"id":"2102.12227","kind":"arxiv","version":3}},"canonical_sha256":"827284344aac53a819558393da6b265811e088c438de5a4acffbd8bff742c45c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"827284344aac53a819558393da6b265811e088c438de5a4acffbd8bff742c45c","first_computed_at":"2026-07-05T06:14:02.162534Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:14:02.162534Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iuF7jCsQJNxKyF6pvo6vLSQeclTzrlCVwK2KyZ8hZkg6puwzbkDLMyeesdhdqoi50IVqz9XzcjLUSXDlIRIwAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:14:02.162932Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.12227","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b74288d02ea370f5f1fbb90f462e4a4a41c0798e7bfc4d645427a7fb962edb06","sha256:a901a91567a89673d6b4b2e775f71233f17151cc6a6a6893ebd4cc374e37ca69"],"state_sha256":"96a7e37bade8a37263d6bab9cc8019979dbab86244dbcfa286e792e1bfd7a297"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wq3uoGq9rhvPRJdRkwKBaEr84DvUkPfwIp4EXdL7u8JfbbH4MKCUTHa5afYVwdVtT6ajazkNXrKKQyRJ8LbwDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T09:33:45.434649Z","bundle_sha256":"8816452b098c5a6f25f20204e61386e48aaa3c2b6f132f1dc893d39d38c004f9"}}