{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2AERR3QQFEJOFTXJJGM3GVFJE3","short_pith_number":"pith:2AERR3QQ","schema_version":"1.0","canonical_sha256":"d00918ee102912e2cee94999b354a926e94e77ce49a83f45af9245d9ded71972","source":{"kind":"arxiv","id":"1909.10166","version":1},"attestation_state":"computed","paper":{"title":"Automatic Short Answer Grading via Multiway Attention Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Gale Yan Huang, Jiliang Tang, Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang, Zitao Liu","submitted_at":"2019-09-23T05:29:04Z","abstract_excerpt":"Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously ext"},"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":"1909.10166","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-09-23T05:29:04Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"8b8f571711bedffacab8f77954783ea0642af3237a555b8861465438512474a3","abstract_canon_sha256":"535bff13cee4e80d711e53506eca66618c7d0a9a28fff7a336969094a7628d32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:25.940912Z","signature_b64":"TdM4Mag4WpoLIn1muprFDv6J7C+4HYT0bKu3OJr2971SS+dcpW6fOsLANL8B8bz00+eaqw9eXWLHvmpASESiDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d00918ee102912e2cee94999b354a926e94e77ce49a83f45af9245d9ded71972","last_reissued_at":"2026-07-05T00:07:25.940509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:25.940509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Short Answer Grading via Multiway Attention Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Gale Yan Huang, Jiliang Tang, Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang, Zitao Liu","submitted_at":"2019-09-23T05:29:04Z","abstract_excerpt":"Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.10166","kind":"arxiv","version":1},"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/1909.10166/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":"1909.10166","created_at":"2026-07-05T00:07:25.940567+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.10166v1","created_at":"2026-07-05T00:07:25.940567+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.10166","created_at":"2026-07-05T00:07:25.940567+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AERR3QQFEJO","created_at":"2026-07-05T00:07:25.940567+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AERR3QQFEJOFTXJ","created_at":"2026-07-05T00:07:25.940567+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AERR3QQ","created_at":"2026-07-05T00:07:25.940567+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.04063","citing_title":"Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3","json":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3.json","graph_json":"https://pith.science/api/pith-number/2AERR3QQFEJOFTXJJGM3GVFJE3/graph.json","events_json":"https://pith.science/api/pith-number/2AERR3QQFEJOFTXJJGM3GVFJE3/events.json","paper":"https://pith.science/paper/2AERR3QQ"},"agent_actions":{"view_html":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3","download_json":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3.json","view_paper":"https://pith.science/paper/2AERR3QQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.10166&json=true","fetch_graph":"https://pith.science/api/pith-number/2AERR3QQFEJOFTXJJGM3GVFJE3/graph.json","fetch_events":"https://pith.science/api/pith-number/2AERR3QQFEJOFTXJJGM3GVFJE3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3/action/storage_attestation","attest_author":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3/action/author_attestation","sign_citation":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3/action/citation_signature","submit_replication":"https://pith.science/pith/2AERR3QQFEJOFTXJJGM3GVFJE3/action/replication_record"}},"created_at":"2026-07-05T00:07:25.940567+00:00","updated_at":"2026-07-05T00:07:25.940567+00:00"}