{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:P3RWYJQH2BQCANWNDFSAT3NOXG","short_pith_number":"pith:P3RWYJQH","schema_version":"1.0","canonical_sha256":"7ee36c2607d0602036cd196409edaeb9991b414266ea924e7c7ba322c9439246","source":{"kind":"arxiv","id":"2412.13110","version":1},"attestation_state":"computed","paper":{"title":"Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Justin Vasselli, Takumi Goto, Taro Watanabe","submitted_at":"2024-12-17T17:31:17Z","abstract_excerpt":"Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for users. To address this issue, we propose attributing sentence-level scores to individual edits, providing insight into how specific corrections contribute to the overall performance. For the attribution method, we use Shapley values, from cooperative game theory, to compute the contr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.13110","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T17:31:17Z","cross_cats_sorted":[],"title_canon_sha256":"7a27cc3303b8f3c4178157e383120dd2a46229314374ad0a22c17eeafe35fac3","abstract_canon_sha256":"824c53c80aab9d94b78ef1df971d9056ba24b340e0a65a86892a1ac5845b9cd8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:40.374000Z","signature_b64":"fTlwxKLplflgS2ellhfnrdQiQX1A+G/NpIvvlqJhoHjgqwQ10QBh/+D8o1LIPia7tLGNToFLct0DpfrISfelDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ee36c2607d0602036cd196409edaeb9991b414266ea924e7c7ba322c9439246","last_reissued_at":"2026-07-05T09:50:40.373519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:40.373519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Justin Vasselli, Takumi Goto, Taro Watanabe","submitted_at":"2024-12-17T17:31:17Z","abstract_excerpt":"Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for users. To address this issue, we propose attributing sentence-level scores to individual edits, providing insight into how specific corrections contribute to the overall performance. For the attribution method, we use Shapley values, from cooperative game theory, to compute the contr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13110","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/2412.13110/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.13110","created_at":"2026-07-05T09:50:40.373587+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13110v1","created_at":"2026-07-05T09:50:40.373587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13110","created_at":"2026-07-05T09:50:40.373587+00:00"},{"alias_kind":"pith_short_12","alias_value":"P3RWYJQH2BQC","created_at":"2026-07-05T09:50:40.373587+00:00"},{"alias_kind":"pith_short_16","alias_value":"P3RWYJQH2BQCANWN","created_at":"2026-07-05T09:50:40.373587+00:00"},{"alias_kind":"pith_short_8","alias_value":"P3RWYJQH","created_at":"2026-07-05T09:50:40.373587+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.19388","citing_title":"gec-metrics: A Unified Library for Grammatical Error Correction Evaluation","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG","json":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG.json","graph_json":"https://pith.science/api/pith-number/P3RWYJQH2BQCANWNDFSAT3NOXG/graph.json","events_json":"https://pith.science/api/pith-number/P3RWYJQH2BQCANWNDFSAT3NOXG/events.json","paper":"https://pith.science/paper/P3RWYJQH"},"agent_actions":{"view_html":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG","download_json":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG.json","view_paper":"https://pith.science/paper/P3RWYJQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13110&json=true","fetch_graph":"https://pith.science/api/pith-number/P3RWYJQH2BQCANWNDFSAT3NOXG/graph.json","fetch_events":"https://pith.science/api/pith-number/P3RWYJQH2BQCANWNDFSAT3NOXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG/action/storage_attestation","attest_author":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG/action/author_attestation","sign_citation":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG/action/citation_signature","submit_replication":"https://pith.science/pith/P3RWYJQH2BQCANWNDFSAT3NOXG/action/replication_record"}},"created_at":"2026-07-05T09:50:40.373587+00:00","updated_at":"2026-07-05T09:50:40.373587+00:00"}