{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:YTBXGZYKW4I6ABSLZJC5FKXF2P","short_pith_number":"pith:YTBXGZYK","schema_version":"1.0","canonical_sha256":"c4c373670ab711e0064bca45d2aae5d3f607f30102f28d2195c0d7eef61537e8","source":{"kind":"arxiv","id":"2202.10287","version":1},"attestation_state":"computed","paper":{"title":"Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexandre Diniz Costa, Ely Edison da Silva Matos, Mateus Coutinho Marim, Tiago Timponi Torrent","submitted_at":"2022-02-21T15:05:23Z","abstract_excerpt":"In this paper we present Scylla, a methodology for domain adaptation of Neural Machine Translation (NMT) systems that make use of a multilingual FrameNet enriched with qualia relations as an external knowledge base. Domain adaptation techniques used in NMT usually require fine-tuning and in-domain training data, which may pose difficulties for those working with lesser-resourced languages and may also lead to performance decay of the NMT system for out-of-domain sentences. Scylla does not require fine-tuning of the NMT model, avoiding the risk of model over-fitting and consequent decrease in p"},"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":"2202.10287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-21T15:05:23Z","cross_cats_sorted":[],"title_canon_sha256":"e7c0e31f5f6bf68e9178dabbf4f569e391c93106aea748ac751192db6aec5d87","abstract_canon_sha256":"a356dab8ddaa01e9d167f569f21a69e9014abf677885da66db3784ab4cab5e1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:58:40.763162Z","signature_b64":"4hZPPPItNjoChF7HcePr6C5CADD0xufIvJ8AegUiAJKLuw2D5rewW2C/kT1M66sE0DGsMkinw0dRNnm+YbgbBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4c373670ab711e0064bca45d2aae5d3f607f30102f28d2195c0d7eef61537e8","last_reissued_at":"2026-07-05T03:58:40.762718Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:58:40.762718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexandre Diniz Costa, Ely Edison da Silva Matos, Mateus Coutinho Marim, Tiago Timponi Torrent","submitted_at":"2022-02-21T15:05:23Z","abstract_excerpt":"In this paper we present Scylla, a methodology for domain adaptation of Neural Machine Translation (NMT) systems that make use of a multilingual FrameNet enriched with qualia relations as an external knowledge base. Domain adaptation techniques used in NMT usually require fine-tuning and in-domain training data, which may pose difficulties for those working with lesser-resourced languages and may also lead to performance decay of the NMT system for out-of-domain sentences. Scylla does not require fine-tuning of the NMT model, avoiding the risk of model over-fitting and consequent decrease in p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.10287","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/2202.10287/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":"2202.10287","created_at":"2026-07-05T03:58:40.762802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.10287v1","created_at":"2026-07-05T03:58:40.762802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.10287","created_at":"2026-07-05T03:58:40.762802+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTBXGZYKW4I6","created_at":"2026-07-05T03:58:40.762802+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTBXGZYKW4I6ABSL","created_at":"2026-07-05T03:58:40.762802+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTBXGZYK","created_at":"2026-07-05T03:58:40.762802+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/YTBXGZYKW4I6ABSLZJC5FKXF2P","json":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P.json","graph_json":"https://pith.science/api/pith-number/YTBXGZYKW4I6ABSLZJC5FKXF2P/graph.json","events_json":"https://pith.science/api/pith-number/YTBXGZYKW4I6ABSLZJC5FKXF2P/events.json","paper":"https://pith.science/paper/YTBXGZYK"},"agent_actions":{"view_html":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P","download_json":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P.json","view_paper":"https://pith.science/paper/YTBXGZYK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.10287&json=true","fetch_graph":"https://pith.science/api/pith-number/YTBXGZYKW4I6ABSLZJC5FKXF2P/graph.json","fetch_events":"https://pith.science/api/pith-number/YTBXGZYKW4I6ABSLZJC5FKXF2P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P/action/storage_attestation","attest_author":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P/action/author_attestation","sign_citation":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P/action/citation_signature","submit_replication":"https://pith.science/pith/YTBXGZYKW4I6ABSLZJC5FKXF2P/action/replication_record"}},"created_at":"2026-07-05T03:58:40.762802+00:00","updated_at":"2026-07-05T03:58:40.762802+00:00"}