{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SFSXYYEREOP5WHXHSBH6FKGIB6","short_pith_number":"pith:SFSXYYER","schema_version":"1.0","canonical_sha256":"91657c6091239fdb1ee7904fe2a8c80faa2df8ceb47576c4249e18b35befe751","source":{"kind":"arxiv","id":"2105.10188","version":2},"attestation_state":"computed","paper":{"title":"Semantic Representation for Dialogue Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Linfeng Song, Xuefeng Bai, Yue Zhang, Yulong Chen","submitted_at":"2021-05-21T07:55:07Z","abstract_excerpt":"Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generat"},"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":"2105.10188","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-21T07:55:07Z","cross_cats_sorted":[],"title_canon_sha256":"c49ae7352c889fe4924e94d3d40127db13173e5db5932a1ac153ceacbf1fcb59","abstract_canon_sha256":"40969a93f2a5d0f07e4c02a63ea0e577836b3bfc255bb591908d52f206f0c9d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:44:59.330014Z","signature_b64":"3d8S/vRXdes/VgQVnnvWC16RG/TKlSDvhWDxlPPgsvepo8ex0g+oPOFQjNWnCVWsQz+a+1Kyhy8Cvc9/SSYgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91657c6091239fdb1ee7904fe2a8c80faa2df8ceb47576c4249e18b35befe751","last_reissued_at":"2026-07-05T02:44:59.329619Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:44:59.329619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Representation for Dialogue Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Linfeng Song, Xuefeng Bai, Yue Zhang, Yulong Chen","submitted_at":"2021-05-21T07:55:07Z","abstract_excerpt":"Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10188","kind":"arxiv","version":2},"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/2105.10188/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":"2105.10188","created_at":"2026-07-05T02:44:59.329682+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.10188v2","created_at":"2026-07-05T02:44:59.329682+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10188","created_at":"2026-07-05T02:44:59.329682+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFSXYYEREOP5","created_at":"2026-07-05T02:44:59.329682+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFSXYYEREOP5WHXH","created_at":"2026-07-05T02:44:59.329682+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFSXYYER","created_at":"2026-07-05T02:44:59.329682+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/SFSXYYEREOP5WHXHSBH6FKGIB6","json":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6.json","graph_json":"https://pith.science/api/pith-number/SFSXYYEREOP5WHXHSBH6FKGIB6/graph.json","events_json":"https://pith.science/api/pith-number/SFSXYYEREOP5WHXHSBH6FKGIB6/events.json","paper":"https://pith.science/paper/SFSXYYER"},"agent_actions":{"view_html":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6","download_json":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6.json","view_paper":"https://pith.science/paper/SFSXYYER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.10188&json=true","fetch_graph":"https://pith.science/api/pith-number/SFSXYYEREOP5WHXHSBH6FKGIB6/graph.json","fetch_events":"https://pith.science/api/pith-number/SFSXYYEREOP5WHXHSBH6FKGIB6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6/action/storage_attestation","attest_author":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6/action/author_attestation","sign_citation":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6/action/citation_signature","submit_replication":"https://pith.science/pith/SFSXYYEREOP5WHXHSBH6FKGIB6/action/replication_record"}},"created_at":"2026-07-05T02:44:59.329682+00:00","updated_at":"2026-07-05T02:44:59.329682+00:00"}