{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:NKVOGC7U6DXEN34M7IJFU45EAH","short_pith_number":"pith:NKVOGC7U","schema_version":"1.0","canonical_sha256":"6aaae30bf4f0ee46ef8cfa125a73a401f7a3e1d118acc0da7e4e94bef256bedd","source":{"kind":"arxiv","id":"2006.10093","version":1},"attestation_state":"computed","paper":{"title":"Extensively Matching for Few-shot Learning Event Detection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Franck Dernoncourt, Thien Huu Nguyen, Viet Dac Lai","submitted_at":"2020-06-17T18:30:30Z","abstract_excerpt":"Current event detection models under super-vised learning settings fail to transfer to newevent types. Few-shot learning has not beenexplored in event detection even though it al-lows a model to perform well with high gener-alization on new event types. In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel. Moreover, these training signals can beapplied in many metric-based few-shot learn-ing models"},"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":"2006.10093","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2020-06-17T18:30:30Z","cross_cats_sorted":[],"title_canon_sha256":"3a75020aff46f61849032e71c584f5664e990740300202923adbceaa131babb0","abstract_canon_sha256":"439ad0722009e2b39a45cb8847d2ac07f9d334014d39c6b1cd5094fef9bd0177"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:14.799222Z","signature_b64":"813Jzz5tV2YtSaZKazjv21Rzv88vY5MVfDqVIJ2GsCiP/BOfByLni9vMfp1LSfi6W1z0rCzU1Y4U/E2zMseBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6aaae30bf4f0ee46ef8cfa125a73a401f7a3e1d118acc0da7e4e94bef256bedd","last_reissued_at":"2026-07-05T01:11:14.798720Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:14.798720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extensively Matching for Few-shot Learning Event Detection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Franck Dernoncourt, Thien Huu Nguyen, Viet Dac Lai","submitted_at":"2020-06-17T18:30:30Z","abstract_excerpt":"Current event detection models under super-vised learning settings fail to transfer to newevent types. Few-shot learning has not beenexplored in event detection even though it al-lows a model to perform well with high gener-alization on new event types. In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel. Moreover, these training signals can beapplied in many metric-based few-shot learn-ing models"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10093","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/2006.10093/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":"2006.10093","created_at":"2026-07-05T01:11:14.798779+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.10093v1","created_at":"2026-07-05T01:11:14.798779+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10093","created_at":"2026-07-05T01:11:14.798779+00:00"},{"alias_kind":"pith_short_12","alias_value":"NKVOGC7U6DXE","created_at":"2026-07-05T01:11:14.798779+00:00"},{"alias_kind":"pith_short_16","alias_value":"NKVOGC7U6DXEN34M","created_at":"2026-07-05T01:11:14.798779+00:00"},{"alias_kind":"pith_short_8","alias_value":"NKVOGC7U","created_at":"2026-07-05T01:11:14.798779+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH","json":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH.json","graph_json":"https://pith.science/api/pith-number/NKVOGC7U6DXEN34M7IJFU45EAH/graph.json","events_json":"https://pith.science/api/pith-number/NKVOGC7U6DXEN34M7IJFU45EAH/events.json","paper":"https://pith.science/paper/NKVOGC7U"},"agent_actions":{"view_html":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH","download_json":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH.json","view_paper":"https://pith.science/paper/NKVOGC7U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.10093&json=true","fetch_graph":"https://pith.science/api/pith-number/NKVOGC7U6DXEN34M7IJFU45EAH/graph.json","fetch_events":"https://pith.science/api/pith-number/NKVOGC7U6DXEN34M7IJFU45EAH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH/action/storage_attestation","attest_author":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH/action/author_attestation","sign_citation":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH/action/citation_signature","submit_replication":"https://pith.science/pith/NKVOGC7U6DXEN34M7IJFU45EAH/action/replication_record"}},"created_at":"2026-07-05T01:11:14.798779+00:00","updated_at":"2026-07-05T01:11:14.798779+00:00"}