{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TO47SJA4LSYXY6KU23MGSVAEFY","short_pith_number":"pith:TO47SJA4","schema_version":"1.0","canonical_sha256":"9bb9f9241c5cb17c7954d6d86954042e2f65a931776841d47948794f8785ec80","source":{"kind":"arxiv","id":"1812.03593","version":5},"attestation_state":"computed","paper":{"title":"SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chenguang Zhu, Michael Zeng, Xuedong Huang","submitted_at":"2018-12-10T01:43:14Z","abstract_excerpt":"Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC) tasks, CQA includes passage comprehension, coreference resolution, and contextual understanding. In this paper, we propose an innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models. Our model leverages both inter-attention and self-attention to comprehend conversation context and extract relevant information from passage. Furthermore, we demonstrated a nove"},"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":"1812.03593","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-10T01:43:14Z","cross_cats_sorted":[],"title_canon_sha256":"44d118328d2a6f064552b9350caad234c96f915b64ce2e4671c67736792a21ea","abstract_canon_sha256":"fe006dc04c5a1847e51321a19d64fcb9c837665fb37be5fd977030d8843af12d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:02.411073Z","signature_b64":"5yiBwdX7noHShQZgsKszwC8WjES+tDcTujj93r4wsUOzoY5ZTRoNx1uazi5K7tzDKLWe507uCDjAj5KBvjXbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bb9f9241c5cb17c7954d6d86954042e2f65a931776841d47948794f8785ec80","last_reissued_at":"2026-05-17T23:57:02.410573Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:02.410573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chenguang Zhu, Michael Zeng, Xuedong Huang","submitted_at":"2018-12-10T01:43:14Z","abstract_excerpt":"Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC) tasks, CQA includes passage comprehension, coreference resolution, and contextual understanding. In this paper, we propose an innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models. Our model leverages both inter-attention and self-attention to comprehend conversation context and extract relevant information from passage. Furthermore, we demonstrated a nove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.03593","kind":"arxiv","version":5},"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":"1812.03593","created_at":"2026-05-17T23:57:02.410649+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.03593v5","created_at":"2026-05-17T23:57:02.410649+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.03593","created_at":"2026-05-17T23:57:02.410649+00:00"},{"alias_kind":"pith_short_12","alias_value":"TO47SJA4LSYX","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TO47SJA4LSYXY6KU","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TO47SJA4","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05716","citing_title":"A Survey of the State-of-the-Art in Conversational Question Answering Systems","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY","json":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY.json","graph_json":"https://pith.science/api/pith-number/TO47SJA4LSYXY6KU23MGSVAEFY/graph.json","events_json":"https://pith.science/api/pith-number/TO47SJA4LSYXY6KU23MGSVAEFY/events.json","paper":"https://pith.science/paper/TO47SJA4"},"agent_actions":{"view_html":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY","download_json":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY.json","view_paper":"https://pith.science/paper/TO47SJA4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.03593&json=true","fetch_graph":"https://pith.science/api/pith-number/TO47SJA4LSYXY6KU23MGSVAEFY/graph.json","fetch_events":"https://pith.science/api/pith-number/TO47SJA4LSYXY6KU23MGSVAEFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY/action/storage_attestation","attest_author":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY/action/author_attestation","sign_citation":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY/action/citation_signature","submit_replication":"https://pith.science/pith/TO47SJA4LSYXY6KU23MGSVAEFY/action/replication_record"}},"created_at":"2026-05-17T23:57:02.410649+00:00","updated_at":"2026-05-17T23:57:02.410649+00:00"}