{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ZONVMLECUVWPGVYSRCETEJHPDV","short_pith_number":"pith:ZONVMLEC","schema_version":"1.0","canonical_sha256":"cb9b562c82a56cf3571288893224ef1d521c78a82c78c211cec836ed0cd75f09","source":{"kind":"arxiv","id":"1611.01734","version":3},"attestation_state":"computed","paper":{"title":"Deep Biaffine Attention for Neural Dependency Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CL","authors_text":"Christopher D. Manning, Timothy Dozat","submitted_at":"2016-11-06T07:26:38Z","abstract_excerpt":"This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016)"},"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":"1611.01734","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-11-06T07:26:38Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"bc9a55c1e4611dff6b235c49fb534e183ed9011479fd1c9c1a39b14aefe12415","abstract_canon_sha256":"83a8701e435e13f8b21be4a7bc5c2452e1edfdf974a5fab6f00edc8a7b8b1f4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:59.747878Z","signature_b64":"iGMipK3kjX455ayJfUReJzurUIn+IJB/BqC71rGjPufOQPln/9bIxMiosVRbOyYCv9uLlqfgWC+zzFkeTEUjCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb9b562c82a56cf3571288893224ef1d521c78a82c78c211cec836ed0cd75f09","last_reissued_at":"2026-05-18T00:48:59.747137Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:59.747137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Biaffine Attention for Neural Dependency Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CL","authors_text":"Christopher D. Manning, Timothy Dozat","submitted_at":"2016-11-06T07:26:38Z","abstract_excerpt":"This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01734","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":""},"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":"1611.01734","created_at":"2026-05-18T00:48:59.747238+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.01734v3","created_at":"2026-05-18T00:48:59.747238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01734","created_at":"2026-05-18T00:48:59.747238+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZONVMLECUVWP","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZONVMLECUVWPGVYS","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZONVMLEC","created_at":"2026-05-18T12:30:55.937587+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1906.11298","citing_title":"A Generative Model for Punctuation in Dependency Trees","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18774","citing_title":"M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10212","citing_title":"Relational Probing: LM-to-Graph Adaptation for Financial Prediction","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02608","citing_title":"Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages","ref_index":86,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV","json":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV.json","graph_json":"https://pith.science/api/pith-number/ZONVMLECUVWPGVYSRCETEJHPDV/graph.json","events_json":"https://pith.science/api/pith-number/ZONVMLECUVWPGVYSRCETEJHPDV/events.json","paper":"https://pith.science/paper/ZONVMLEC"},"agent_actions":{"view_html":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV","download_json":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV.json","view_paper":"https://pith.science/paper/ZONVMLEC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.01734&json=true","fetch_graph":"https://pith.science/api/pith-number/ZONVMLECUVWPGVYSRCETEJHPDV/graph.json","fetch_events":"https://pith.science/api/pith-number/ZONVMLECUVWPGVYSRCETEJHPDV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV/action/storage_attestation","attest_author":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV/action/author_attestation","sign_citation":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV/action/citation_signature","submit_replication":"https://pith.science/pith/ZONVMLECUVWPGVYSRCETEJHPDV/action/replication_record"}},"created_at":"2026-05-18T00:48:59.747238+00:00","updated_at":"2026-05-18T00:48:59.747238+00:00"}