{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6SQRSM6R2KOHYQAMKP5MAQXKYD","short_pith_number":"pith:6SQRSM6R","canonical_record":{"source":{"id":"2205.01464","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T12:58:36Z","cross_cats_sorted":[],"title_canon_sha256":"7c1c1b712edb2af30ca1ab12c76b38a13f05809aa463e775347a4881daa4f8f7","abstract_canon_sha256":"b8b6de10a1feec294455683f2dc378d5119532b2d678eae76d7ac1981e9626dc"},"schema_version":"1.0"},"canonical_sha256":"f4a11933d1d29c7c400c53fac042eac0f9aea1dbbc009cc9d6b286a3e5313ed2","source":{"kind":"arxiv","id":"2205.01464","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01464","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01464v1","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01464","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_12","alias_value":"6SQRSM6R2KOH","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_16","alias_value":"6SQRSM6R2KOHYQAM","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_8","alias_value":"6SQRSM6R","created_at":"2026-07-05T04:20:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6SQRSM6R2KOHYQAMKP5MAQXKYD","target":"record","payload":{"canonical_record":{"source":{"id":"2205.01464","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T12:58:36Z","cross_cats_sorted":[],"title_canon_sha256":"7c1c1b712edb2af30ca1ab12c76b38a13f05809aa463e775347a4881daa4f8f7","abstract_canon_sha256":"b8b6de10a1feec294455683f2dc378d5119532b2d678eae76d7ac1981e9626dc"},"schema_version":"1.0"},"canonical_sha256":"f4a11933d1d29c7c400c53fac042eac0f9aea1dbbc009cc9d6b286a3e5313ed2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:04.990742Z","signature_b64":"TR0AxIa6/yjpXTt7N6GppfhRrzMD2L59+9SLDze0PdVqwo4YidyPcEMxY8HkS8R+BCR6hkVnfEnmJWy97bUlDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4a11933d1d29c7c400c53fac042eac0f9aea1dbbc009cc9d6b286a3e5313ed2","last_reissued_at":"2026-07-05T04:20:04.990343Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:04.990343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.01464","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":"2026-07-05T04:20:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bky8x96hskPw2Vz4DHjLQxVzRy9WeCVpntdSEdTy8UHz9JzU5GJQhrc1wpdRhNhuZrod9ywH/C/g0F65npMJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T05:35:24.738683Z"},"content_sha256":"98f68185176d84de31eb9c8108188d84c57f91cfce0c2ac1674629268db6ec77","schema_version":"1.0","event_id":"sha256:98f68185176d84de31eb9c8108188d84c57f91cfce0c2ac1674629268db6ec77"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6SQRSM6R2KOHYQAMKP5MAQXKYD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inducing and Using Alignments for Transition-based AMR Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andrew Drozdov, Andrew McCallum, Jiawei Zhou, Radu Florian, Ramon Fernandez Astudillo, Tahira Naseem, Yoon Kim","submitted_at":"2022-05-03T12:58:36Z","abstract_excerpt":"Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints. Parsers also train on a point-estimate of the alignment pipeline, neglecting the uncertainty due to the inherent ambiguity of alignment. In this work we explore two avenues for overcoming these limitations. First, we propose a neural aligner for AMR that learns node-to-word alignments without relying on c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01464","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.01464/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":"2026-07-05T04:20:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sLFF42/WmRowkv9nByGJKtffihVD94iUukDntn3MJs4zbB0a5WZgb0N37QU3E6YGo5Z8hbtBqYB9v8SpvwjQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T05:35:24.739179Z"},"content_sha256":"7991067eb6e6f70853c2be4b80c336faac4a9817846032ba4be39030cf2c4776","schema_version":"1.0","event_id":"sha256:7991067eb6e6f70853c2be4b80c336faac4a9817846032ba4be39030cf2c4776"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/bundle.json","state_url":"https://pith.science/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/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":"2026-08-11T05:35:24Z","links":{"resolver":"https://pith.science/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD","bundle":"https://pith.science/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/bundle.json","state":"https://pith.science/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6SQRSM6R2KOHYQAMKP5MAQXKYD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6SQRSM6R2KOHYQAMKP5MAQXKYD","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":"b8b6de10a1feec294455683f2dc378d5119532b2d678eae76d7ac1981e9626dc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T12:58:36Z","title_canon_sha256":"7c1c1b712edb2af30ca1ab12c76b38a13f05809aa463e775347a4881daa4f8f7"},"schema_version":"1.0","source":{"id":"2205.01464","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01464","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01464v1","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01464","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_12","alias_value":"6SQRSM6R2KOH","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_16","alias_value":"6SQRSM6R2KOHYQAM","created_at":"2026-07-05T04:20:04Z"},{"alias_kind":"pith_short_8","alias_value":"6SQRSM6R","created_at":"2026-07-05T04:20:04Z"}],"graph_snapshots":[{"event_id":"sha256:7991067eb6e6f70853c2be4b80c336faac4a9817846032ba4be39030cf2c4776","target":"graph","created_at":"2026-07-05T04:20:04Z","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/2205.01464/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints. Parsers also train on a point-estimate of the alignment pipeline, neglecting the uncertainty due to the inherent ambiguity of alignment. In this work we explore two avenues for overcoming these limitations. First, we propose a neural aligner for AMR that learns node-to-word alignments without relying on c","authors_text":"Andrew Drozdov, Andrew McCallum, Jiawei Zhou, Radu Florian, Ramon Fernandez Astudillo, Tahira Naseem, Yoon Kim","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T12:58:36Z","title":"Inducing and Using Alignments for Transition-based AMR Parsing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01464","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":"sha256:98f68185176d84de31eb9c8108188d84c57f91cfce0c2ac1674629268db6ec77","target":"record","created_at":"2026-07-05T04:20:04Z","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":"b8b6de10a1feec294455683f2dc378d5119532b2d678eae76d7ac1981e9626dc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T12:58:36Z","title_canon_sha256":"7c1c1b712edb2af30ca1ab12c76b38a13f05809aa463e775347a4881daa4f8f7"},"schema_version":"1.0","source":{"id":"2205.01464","kind":"arxiv","version":1}},"canonical_sha256":"f4a11933d1d29c7c400c53fac042eac0f9aea1dbbc009cc9d6b286a3e5313ed2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4a11933d1d29c7c400c53fac042eac0f9aea1dbbc009cc9d6b286a3e5313ed2","first_computed_at":"2026-07-05T04:20:04.990343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:20:04.990343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TR0AxIa6/yjpXTt7N6GppfhRrzMD2L59+9SLDze0PdVqwo4YidyPcEMxY8HkS8R+BCR6hkVnfEnmJWy97bUlDw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:20:04.990742Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.01464","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98f68185176d84de31eb9c8108188d84c57f91cfce0c2ac1674629268db6ec77","sha256:7991067eb6e6f70853c2be4b80c336faac4a9817846032ba4be39030cf2c4776"],"state_sha256":"05aedd0d74b1c6ce6a8b68b7bf83935db1fb136f0c07c466f1d3efd59cd9ec45"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x0EVVmpjRurLeK6Rwe+ODwDWuzv0QZgHg2LaPAU0gQ+PUizPfpewIXfjSOlMQ8U/xF0ELK1TNynwSpC2cq/lDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T05:35:24.743502Z","bundle_sha256":"4a98344453b328833d60de347ba7df21da40db3df79939f84b46e5a7c16fcf7e"}}