{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QTXQDAX3UISQLXA7EPLHK6ZQBZ","short_pith_number":"pith:QTXQDAX3","schema_version":"1.0","canonical_sha256":"84ef0182fba22505dc1f23d6757b300e730940684376a7893da5093faf26df69","source":{"kind":"arxiv","id":"2112.08327","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Karthik Raghunathan, Sai Muralidhar Jayanthi, Varsha Embar","submitted_at":"2021-12-15T18:20:12Z","abstract_excerpt":"The wide applicability of pretrained transformer models (PTMs) for natural language tasks is well demonstrated, but their ability to comprehend short phrases of text is less explored. To this end, we evaluate different PTMs from the lens of unsupervised Entity Linking in task-oriented dialog across 5 characteristics -- syntactic, semantic, short-forms, numeric and phonetic. Our results demonstrate that several of the PTMs produce sub-par results when compared to traditional techniques, albeit competitive to other neural baselines. We find that some of their shortcomings can be addressed by usi"},"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":"2112.08327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-15T18:20:12Z","cross_cats_sorted":[],"title_canon_sha256":"748d0e3c31649d022f80fac11393022b52da2b00405bde39e878d28bebfc0e61","abstract_canon_sha256":"aeacfd11e6ebc0ec226ccd118473b29f520402773bc1f0df0aea16bbb38fc075"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:20.917143Z","signature_b64":"Pkg5ffs7ESHj2gQUWHqY70ZPVijpDs/lEafuU6HDrU7VxbEnbYmKNA+5j4G5Y0QWUHRhFpKwKkfB7a4U1e/aBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84ef0182fba22505dc1f23d6757b300e730940684376a7893da5093faf26df69","last_reissued_at":"2026-07-05T03:41:20.916713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:20.916713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Karthik Raghunathan, Sai Muralidhar Jayanthi, Varsha Embar","submitted_at":"2021-12-15T18:20:12Z","abstract_excerpt":"The wide applicability of pretrained transformer models (PTMs) for natural language tasks is well demonstrated, but their ability to comprehend short phrases of text is less explored. To this end, we evaluate different PTMs from the lens of unsupervised Entity Linking in task-oriented dialog across 5 characteristics -- syntactic, semantic, short-forms, numeric and phonetic. Our results demonstrate that several of the PTMs produce sub-par results when compared to traditional techniques, albeit competitive to other neural baselines. We find that some of their shortcomings can be addressed by usi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08327","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/2112.08327/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":"2112.08327","created_at":"2026-07-05T03:41:20.916771+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.08327v1","created_at":"2026-07-05T03:41:20.916771+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08327","created_at":"2026-07-05T03:41:20.916771+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTXQDAX3UISQ","created_at":"2026-07-05T03:41:20.916771+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTXQDAX3UISQLXA7","created_at":"2026-07-05T03:41:20.916771+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTXQDAX3","created_at":"2026-07-05T03:41:20.916771+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.16754","citing_title":"Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ","json":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ.json","graph_json":"https://pith.science/api/pith-number/QTXQDAX3UISQLXA7EPLHK6ZQBZ/graph.json","events_json":"https://pith.science/api/pith-number/QTXQDAX3UISQLXA7EPLHK6ZQBZ/events.json","paper":"https://pith.science/paper/QTXQDAX3"},"agent_actions":{"view_html":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ","download_json":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ.json","view_paper":"https://pith.science/paper/QTXQDAX3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.08327&json=true","fetch_graph":"https://pith.science/api/pith-number/QTXQDAX3UISQLXA7EPLHK6ZQBZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QTXQDAX3UISQLXA7EPLHK6ZQBZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ/action/storage_attestation","attest_author":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ/action/author_attestation","sign_citation":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ/action/citation_signature","submit_replication":"https://pith.science/pith/QTXQDAX3UISQLXA7EPLHK6ZQBZ/action/replication_record"}},"created_at":"2026-07-05T03:41:20.916771+00:00","updated_at":"2026-07-05T03:41:20.916771+00:00"}