{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QH5QLDCLEVYWZLYKXZA4HSRLHK","short_pith_number":"pith:QH5QLDCL","schema_version":"1.0","canonical_sha256":"81fb058c4b25716caf0abe41c3ca2b3a9ad3a949c9018a435f8d5561171fcff8","source":{"kind":"arxiv","id":"1902.08649","version":3},"attestation_state":"computed","paper":{"title":"Saliency Learning: Teaching the Model Where to Pay Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Hamed Shahbazi, Prasad Tadepalli, Reza Ghaeini, Xiaoli Z. Fern","submitted_at":"2019-02-22T19:38:36Z","abstract_excerpt":"Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behaviour and predictions, which are helpful for assessing the reliability of the model's predictions. However, such methods do not improve the model's reliability. In this paper, we aim to teach the model to make the right prediction for the right reason by providing explanation training and ensuring the alignment of th"},"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":"1902.08649","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-22T19:38:36Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8ba412aa148c6e4285027eba8db19b167f680f88162f5bbf9234cef6755014eb","abstract_canon_sha256":"9be2d1adbc1ece9ccc502bc2f0e128839acc81ac59ea39da2e264cac285bc095"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:51.965053Z","signature_b64":"16ZyPfWMdRhuH6zUnj+miKkI0H5xSVxDTAwGms1+6EfGoooVCdvy9fBkimjqKpIx3xwJyqm6+HyehN5W/+VnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81fb058c4b25716caf0abe41c3ca2b3a9ad3a949c9018a435f8d5561171fcff8","last_reissued_at":"2026-05-17T23:45:51.964580Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:51.964580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Saliency Learning: Teaching the Model Where to Pay Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Hamed Shahbazi, Prasad Tadepalli, Reza Ghaeini, Xiaoli Z. Fern","submitted_at":"2019-02-22T19:38:36Z","abstract_excerpt":"Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behaviour and predictions, which are helpful for assessing the reliability of the model's predictions. However, such methods do not improve the model's reliability. In this paper, we aim to teach the model to make the right prediction for the right reason by providing explanation training and ensuring the alignment of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.08649","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":"1902.08649","created_at":"2026-05-17T23:45:51.964654+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.08649v3","created_at":"2026-05-17T23:45:51.964654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.08649","created_at":"2026-05-17T23:45:51.964654+00:00"},{"alias_kind":"pith_short_12","alias_value":"QH5QLDCLEVYW","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QH5QLDCLEVYWZLYK","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QH5QLDCL","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.25315","citing_title":"SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK","json":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK.json","graph_json":"https://pith.science/api/pith-number/QH5QLDCLEVYWZLYKXZA4HSRLHK/graph.json","events_json":"https://pith.science/api/pith-number/QH5QLDCLEVYWZLYKXZA4HSRLHK/events.json","paper":"https://pith.science/paper/QH5QLDCL"},"agent_actions":{"view_html":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK","download_json":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK.json","view_paper":"https://pith.science/paper/QH5QLDCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.08649&json=true","fetch_graph":"https://pith.science/api/pith-number/QH5QLDCLEVYWZLYKXZA4HSRLHK/graph.json","fetch_events":"https://pith.science/api/pith-number/QH5QLDCLEVYWZLYKXZA4HSRLHK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK/action/storage_attestation","attest_author":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK/action/author_attestation","sign_citation":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK/action/citation_signature","submit_replication":"https://pith.science/pith/QH5QLDCLEVYWZLYKXZA4HSRLHK/action/replication_record"}},"created_at":"2026-05-17T23:45:51.964654+00:00","updated_at":"2026-05-17T23:45:51.964654+00:00"}