{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:IOXWDMOUIYEG66XHNRHHW4QI34","short_pith_number":"pith:IOXWDMOU","canonical_record":{"source":{"id":"1704.06104","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T12:20:43Z","cross_cats_sorted":[],"title_canon_sha256":"496a8a1597dc19e6f5b2964dead5cbc7e7cbd4380413b5fe5ee28aa8333245b9","abstract_canon_sha256":"014f5475f76ea854a6f5ba17120c562e6d54c667a011c1ccd97645f229f30e77"},"schema_version":"1.0"},"canonical_sha256":"43af61b1d446086f7ae76c4e7b7208df3c0872a2e9cdfbd8a514351373d4de45","source":{"kind":"arxiv","id":"1704.06104","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06104","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06104v2","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06104","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"pith_short_12","alias_value":"IOXWDMOUIYEG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IOXWDMOUIYEG66XH","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IOXWDMOU","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:IOXWDMOUIYEG66XHNRHHW4QI34","target":"record","payload":{"canonical_record":{"source":{"id":"1704.06104","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T12:20:43Z","cross_cats_sorted":[],"title_canon_sha256":"496a8a1597dc19e6f5b2964dead5cbc7e7cbd4380413b5fe5ee28aa8333245b9","abstract_canon_sha256":"014f5475f76ea854a6f5ba17120c562e6d54c667a011c1ccd97645f229f30e77"},"schema_version":"1.0"},"canonical_sha256":"43af61b1d446086f7ae76c4e7b7208df3c0872a2e9cdfbd8a514351373d4de45","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:58.002204Z","signature_b64":"2RNYuDJ9Ty18AIX6qSZ7t1XY7k1dkIU0gw7esumG7WlKlHhH4UYrzV1h75Ik9fDVdBg+vO8vtb6eoqLgKfgsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43af61b1d446086f7ae76c4e7b7208df3c0872a2e9cdfbd8a514351373d4de45","last_reissued_at":"2026-05-18T00:45:58.001632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:58.001632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.06104","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":"2026-05-18T00:45:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mf7kuCelosEUkts0HiBw5aAONUEOc9psrOQev0D6j0zrL6gntTHRjmkjsTVsyYUynbI5aJV0UfGXhGsjcZDyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T17:53:25.832453Z"},"content_sha256":"5f7c420c6cf96fadb4b7c473120c7d9303bbf5db0ee0ac9a606ddbc134ac5e18","schema_version":"1.0","event_id":"sha256:5f7c420c6cf96fadb4b7c473120c7d9303bbf5db0ee0ac9a606ddbc134ac5e18"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:IOXWDMOUIYEG66XHNRHHW4QI34","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural End-to-End Learning for Computational Argumentation Mining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Johannes Daxenberger, Steffen Eger","submitted_at":"2017-04-20T12:20:43Z","abstract_excerpt":"We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06104","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":""},"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":"2026-05-18T00:45:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ewn/xOVg3SGmh29KIYXQ/tnnFIfl6B9mXE89VcwBSfMI/ujVypl3muengSaj1K1uLxRm19BAgMhE4Poojn43Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T17:53:25.832803Z"},"content_sha256":"0d6d1e8b8729aa76c7568accf721cc4420df70237f7ec5c97d7de4e7a27c2d3d","schema_version":"1.0","event_id":"sha256:0d6d1e8b8729aa76c7568accf721cc4420df70237f7ec5c97d7de4e7a27c2d3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IOXWDMOUIYEG66XHNRHHW4QI34/bundle.json","state_url":"https://pith.science/pith/IOXWDMOUIYEG66XHNRHHW4QI34/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IOXWDMOUIYEG66XHNRHHW4QI34/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":"2026-06-24T17:53:25Z","links":{"resolver":"https://pith.science/pith/IOXWDMOUIYEG66XHNRHHW4QI34","bundle":"https://pith.science/pith/IOXWDMOUIYEG66XHNRHHW4QI34/bundle.json","state":"https://pith.science/pith/IOXWDMOUIYEG66XHNRHHW4QI34/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IOXWDMOUIYEG66XHNRHHW4QI34/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:IOXWDMOUIYEG66XHNRHHW4QI34","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":"014f5475f76ea854a6f5ba17120c562e6d54c667a011c1ccd97645f229f30e77","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T12:20:43Z","title_canon_sha256":"496a8a1597dc19e6f5b2964dead5cbc7e7cbd4380413b5fe5ee28aa8333245b9"},"schema_version":"1.0","source":{"id":"1704.06104","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06104","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06104v2","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06104","created_at":"2026-05-18T00:45:58Z"},{"alias_kind":"pith_short_12","alias_value":"IOXWDMOUIYEG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IOXWDMOUIYEG66XH","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IOXWDMOU","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:0d6d1e8b8729aa76c7568accf721cc4420df70237f7ec5c97d7de4e7a27c2d3d","target":"graph","created_at":"2026-05-18T00:45:58Z","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 investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learn","authors_text":"Iryna Gurevych, Johannes Daxenberger, Steffen Eger","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T12:20:43Z","title":"Neural End-to-End Learning for Computational Argumentation Mining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06104","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":"sha256:5f7c420c6cf96fadb4b7c473120c7d9303bbf5db0ee0ac9a606ddbc134ac5e18","target":"record","created_at":"2026-05-18T00:45:58Z","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":"014f5475f76ea854a6f5ba17120c562e6d54c667a011c1ccd97645f229f30e77","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-20T12:20:43Z","title_canon_sha256":"496a8a1597dc19e6f5b2964dead5cbc7e7cbd4380413b5fe5ee28aa8333245b9"},"schema_version":"1.0","source":{"id":"1704.06104","kind":"arxiv","version":2}},"canonical_sha256":"43af61b1d446086f7ae76c4e7b7208df3c0872a2e9cdfbd8a514351373d4de45","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43af61b1d446086f7ae76c4e7b7208df3c0872a2e9cdfbd8a514351373d4de45","first_computed_at":"2026-05-18T00:45:58.001632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:58.001632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2RNYuDJ9Ty18AIX6qSZ7t1XY7k1dkIU0gw7esumG7WlKlHhH4UYrzV1h75Ik9fDVdBg+vO8vtb6eoqLgKfgsDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:58.002204Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.06104","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f7c420c6cf96fadb4b7c473120c7d9303bbf5db0ee0ac9a606ddbc134ac5e18","sha256:0d6d1e8b8729aa76c7568accf721cc4420df70237f7ec5c97d7de4e7a27c2d3d"],"state_sha256":"bddfa54ce180108bfc6814cc909c79cfadb0e62b55497c18da8c7f5fa80cf9b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZxZDy0wgbj8jOezoe2UA6BUQsOuo0M6/92g5P8itT0vxRlTDxoTXp68e9qe5Fm7ceeITpfDezT0GQiyejrABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T17:53:25.834771Z","bundle_sha256":"01b3db922000d840fbe9abf4b8ef8a8a33ea204aa1b623518092b00f0251908d"}}