{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6JOBG4ZTJQNFONI2D4L3WNL27C","short_pith_number":"pith:6JOBG4ZT","schema_version":"1.0","canonical_sha256":"f25c1373334c1a57351a1f17bb357af88ea16e2abcc9385af7206f6bc8cc3139","source":{"kind":"arxiv","id":"1510.03710","version":3},"attestation_state":"computed","paper":{"title":"Hybrid Dialog State Tracker","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jan Kleindienst, Miroslav Vodol\\'an, Rudolf Kadlec","submitted_at":"2015-10-13T14:44:01Z","abstract_excerpt":"This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new state-of-the-art result for the Dialog State Tracking Challenge (DSTC) 2 dataset when the system uses only live SLU as its input."},"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":"1510.03710","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-10-13T14:44:01Z","cross_cats_sorted":[],"title_canon_sha256":"64a9d77268a0cc4f62f6a3ba7c0a73e901a25e0a0a64607ac8629d7cab269712","abstract_canon_sha256":"3eafc329c51f04c1eadcff95afc8ae33aa6dd7956c433cec54b534f47ec725db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:53.832997Z","signature_b64":"n927u8CA3418xV3wB/luOicdrV+zedlgtswV6sF/MewUDFU7jS0inHQn/cVcMr1T18Ps8h8C6dvEJADekEaXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f25c1373334c1a57351a1f17bb357af88ea16e2abcc9385af7206f6bc8cc3139","last_reissued_at":"2026-05-18T01:22:53.832244Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:53.832244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Dialog State Tracker","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jan Kleindienst, Miroslav Vodol\\'an, Rudolf Kadlec","submitted_at":"2015-10-13T14:44:01Z","abstract_excerpt":"This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new state-of-the-art result for the Dialog State Tracking Challenge (DSTC) 2 dataset when the system uses only live SLU as its input."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.03710","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":"1510.03710","created_at":"2026-05-18T01:22:53.832377+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.03710v3","created_at":"2026-05-18T01:22:53.832377+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.03710","created_at":"2026-05-18T01:22:53.832377+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JOBG4ZTJQNF","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JOBG4ZTJQNFONI2","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JOBG4ZT","created_at":"2026-05-18T12:29:07.941421+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/6JOBG4ZTJQNFONI2D4L3WNL27C","json":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C.json","graph_json":"https://pith.science/api/pith-number/6JOBG4ZTJQNFONI2D4L3WNL27C/graph.json","events_json":"https://pith.science/api/pith-number/6JOBG4ZTJQNFONI2D4L3WNL27C/events.json","paper":"https://pith.science/paper/6JOBG4ZT"},"agent_actions":{"view_html":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C","download_json":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C.json","view_paper":"https://pith.science/paper/6JOBG4ZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.03710&json=true","fetch_graph":"https://pith.science/api/pith-number/6JOBG4ZTJQNFONI2D4L3WNL27C/graph.json","fetch_events":"https://pith.science/api/pith-number/6JOBG4ZTJQNFONI2D4L3WNL27C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C/action/storage_attestation","attest_author":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C/action/author_attestation","sign_citation":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C/action/citation_signature","submit_replication":"https://pith.science/pith/6JOBG4ZTJQNFONI2D4L3WNL27C/action/replication_record"}},"created_at":"2026-05-18T01:22:53.832377+00:00","updated_at":"2026-05-18T01:22:53.832377+00:00"}