{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:O3A6WZOTMKFLKEDRJULTHIPSSY","short_pith_number":"pith:O3A6WZOT","schema_version":"1.0","canonical_sha256":"76c1eb65d3628ab510714d1733a1f29606fd72b403861bfc3bfd23e96b2d45f6","source":{"kind":"arxiv","id":"2012.14756","version":3},"attestation_state":"computed","paper":{"title":"Dialogue Response Selection with Hierarchical Curriculum Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Deng Cai, Nigel Collier, Qingyu Zhou, Shuming Shi, Simon Baker, Yan Wang, Yixuan Su, Yunbo Cao, Zibo Lin","submitted_at":"2020-12-29T14:06:41Z","abstract_excerpt":"We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an \"easy-to-difficult\" scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, i"},"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":"2012.14756","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2020-12-29T14:06:41Z","cross_cats_sorted":[],"title_canon_sha256":"9494ae3ddbcdeaeed0057864117bbb2f477e6f827b05383bdf0a0771a5402450","abstract_canon_sha256":"42d2a37f0b8e29e57d43bd029dc46080ad8119587e29a986086bbca2e0e97f55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:01.087473Z","signature_b64":"Sb5bgb+PkKkQBI9fw7Msr9gD/Ck+KaqAz9H25eRAk9HOIdLOParY48Ma8vJF5tNoKGhNXhiqbIzleV6BW0k6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76c1eb65d3628ab510714d1733a1f29606fd72b403861bfc3bfd23e96b2d45f6","last_reissued_at":"2026-07-05T03:10:01.087010Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:01.087010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dialogue Response Selection with Hierarchical Curriculum Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Deng Cai, Nigel Collier, Qingyu Zhou, Shuming Shi, Simon Baker, Yan Wang, Yixuan Su, Yunbo Cao, Zibo Lin","submitted_at":"2020-12-29T14:06:41Z","abstract_excerpt":"We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an \"easy-to-difficult\" scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.14756","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2012.14756/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":"2012.14756","created_at":"2026-07-05T03:10:01.087066+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.14756v3","created_at":"2026-07-05T03:10:01.087066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.14756","created_at":"2026-07-05T03:10:01.087066+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3A6WZOTMKFL","created_at":"2026-07-05T03:10:01.087066+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3A6WZOTMKFLKEDR","created_at":"2026-07-05T03:10:01.087066+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3A6WZOT","created_at":"2026-07-05T03:10:01.087066+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/O3A6WZOTMKFLKEDRJULTHIPSSY","json":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY.json","graph_json":"https://pith.science/api/pith-number/O3A6WZOTMKFLKEDRJULTHIPSSY/graph.json","events_json":"https://pith.science/api/pith-number/O3A6WZOTMKFLKEDRJULTHIPSSY/events.json","paper":"https://pith.science/paper/O3A6WZOT"},"agent_actions":{"view_html":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY","download_json":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY.json","view_paper":"https://pith.science/paper/O3A6WZOT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.14756&json=true","fetch_graph":"https://pith.science/api/pith-number/O3A6WZOTMKFLKEDRJULTHIPSSY/graph.json","fetch_events":"https://pith.science/api/pith-number/O3A6WZOTMKFLKEDRJULTHIPSSY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY/action/storage_attestation","attest_author":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY/action/author_attestation","sign_citation":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY/action/citation_signature","submit_replication":"https://pith.science/pith/O3A6WZOTMKFLKEDRJULTHIPSSY/action/replication_record"}},"created_at":"2026-07-05T03:10:01.087066+00:00","updated_at":"2026-07-05T03:10:01.087066+00:00"}