{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CEHJB2UA37STC5RQ2Z2LQ2VW7L","short_pith_number":"pith:CEHJB2UA","schema_version":"1.0","canonical_sha256":"110e90ea80dfe5317630d674b86ab6fad8f779889c997e7b08fedc35f28c3531","source":{"kind":"arxiv","id":"2109.04312","version":1},"attestation_state":"computed","paper":{"title":"MATE: Multi-view Attention for Table Transformer Efficiency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Julian Martin Eisenschlos, Maharshi Gor, Thomas M\\\"uller, William W. Cohen","submitted_at":"2021-09-09T14:39:30Z","abstract_excerpt":"This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens. Here we propose MATE, a novel Transformer architecture designed to model the structure of web tables. MATE uses sparse attention in a way that allows heads to efficiently attend to either rows or columns "},"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":"2109.04312","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-09T14:39:30Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"5378b61a46172365d14f931588d99282489539107617aecfed81e75887e5e774","abstract_canon_sha256":"121ea5a68de2d7d6b2477654e7eb1323940c0a056c677e347bbc64124a702fe5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:57.368802Z","signature_b64":"NmuudGmnDak2ihGz5pg0YOBXuJsqs6s3CzLFiKIE11GAXzARLHA4FILjLuPsAp47Fw+CBMEjYkOQhPsK+S6mDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"110e90ea80dfe5317630d674b86ab6fad8f779889c997e7b08fedc35f28c3531","last_reissued_at":"2026-07-05T03:12:57.368330Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:57.368330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MATE: Multi-view Attention for Table Transformer Efficiency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Julian Martin Eisenschlos, Maharshi Gor, Thomas M\\\"uller, William W. Cohen","submitted_at":"2021-09-09T14:39:30Z","abstract_excerpt":"This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens. Here we propose MATE, a novel Transformer architecture designed to model the structure of web tables. MATE uses sparse attention in a way that allows heads to efficiently attend to either rows or columns "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04312","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/2109.04312/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":"2109.04312","created_at":"2026-07-05T03:12:57.368387+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.04312v1","created_at":"2026-07-05T03:12:57.368387+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04312","created_at":"2026-07-05T03:12:57.368387+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEHJB2UA37ST","created_at":"2026-07-05T03:12:57.368387+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEHJB2UA37STC5RQ","created_at":"2026-07-05T03:12:57.368387+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEHJB2UA","created_at":"2026-07-05T03:12:57.368387+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.17767","citing_title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L","json":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L.json","graph_json":"https://pith.science/api/pith-number/CEHJB2UA37STC5RQ2Z2LQ2VW7L/graph.json","events_json":"https://pith.science/api/pith-number/CEHJB2UA37STC5RQ2Z2LQ2VW7L/events.json","paper":"https://pith.science/paper/CEHJB2UA"},"agent_actions":{"view_html":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L","download_json":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L.json","view_paper":"https://pith.science/paper/CEHJB2UA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.04312&json=true","fetch_graph":"https://pith.science/api/pith-number/CEHJB2UA37STC5RQ2Z2LQ2VW7L/graph.json","fetch_events":"https://pith.science/api/pith-number/CEHJB2UA37STC5RQ2Z2LQ2VW7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L/action/storage_attestation","attest_author":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L/action/author_attestation","sign_citation":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L/action/citation_signature","submit_replication":"https://pith.science/pith/CEHJB2UA37STC5RQ2Z2LQ2VW7L/action/replication_record"}},"created_at":"2026-07-05T03:12:57.368387+00:00","updated_at":"2026-07-05T03:12:57.368387+00:00"}