{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HISROGLWP5VJO5NWOZEKB4BUSC","short_pith_number":"pith:HISROGLW","schema_version":"1.0","canonical_sha256":"3a251719767f6a9775b67648a0f03490ad54bfe80b75646a5ceb24b4071e7577","source":{"kind":"arxiv","id":"1707.05227","version":1},"attestation_state":"computed","paper":{"title":"Auxiliary Objectives for Neural Error Detection Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Helen Yannakoudakis, Marek Rei","submitted_at":"2017-07-17T15:24:09Z","abstract_excerpt":"We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objecti"},"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":"1707.05227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-07-17T15:24:09Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"6a11597845c40c5ca395be6b8303bb539f886cc16bc0c3bdcbea05f7b2c53e23","abstract_canon_sha256":"567301aaacc2af3b77d91ba74f22c7243eb42dd4ca91c85d4599ad37e4a271aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:09.134110Z","signature_b64":"eENk+UkedH514eSKp4GTa0pL+Bl+RzhWw12OSmIbzB2hhBrZcbl3XjsZwWJq7p06sCxcV35Rz9kFrmuexaovBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a251719767f6a9775b67648a0f03490ad54bfe80b75646a5ceb24b4071e7577","last_reissued_at":"2026-05-18T00:40:09.133622Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:09.133622Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Auxiliary Objectives for Neural Error Detection Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Helen Yannakoudakis, Marek Rei","submitted_at":"2017-07-17T15:24:09Z","abstract_excerpt":"We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05227","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":""},"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":"1707.05227","created_at":"2026-05-18T00:40:09.133688+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.05227v1","created_at":"2026-05-18T00:40:09.133688+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05227","created_at":"2026-05-18T00:40:09.133688+00:00"},{"alias_kind":"pith_short_12","alias_value":"HISROGLWP5VJ","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HISROGLWP5VJO5NW","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HISROGLW","created_at":"2026-05-18T12:31:18.294218+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/HISROGLWP5VJO5NWOZEKB4BUSC","json":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC.json","graph_json":"https://pith.science/api/pith-number/HISROGLWP5VJO5NWOZEKB4BUSC/graph.json","events_json":"https://pith.science/api/pith-number/HISROGLWP5VJO5NWOZEKB4BUSC/events.json","paper":"https://pith.science/paper/HISROGLW"},"agent_actions":{"view_html":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC","download_json":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC.json","view_paper":"https://pith.science/paper/HISROGLW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.05227&json=true","fetch_graph":"https://pith.science/api/pith-number/HISROGLWP5VJO5NWOZEKB4BUSC/graph.json","fetch_events":"https://pith.science/api/pith-number/HISROGLWP5VJO5NWOZEKB4BUSC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC/action/storage_attestation","attest_author":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC/action/author_attestation","sign_citation":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC/action/citation_signature","submit_replication":"https://pith.science/pith/HISROGLWP5VJO5NWOZEKB4BUSC/action/replication_record"}},"created_at":"2026-05-18T00:40:09.133688+00:00","updated_at":"2026-05-18T00:40:09.133688+00:00"}