{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H4ISNY7M3OD3T4F35RAGFD76QV","short_pith_number":"pith:H4ISNY7M","schema_version":"1.0","canonical_sha256":"3f1126e3ecdb87b9f0bbec40628ffe8540c7fbe1cd9554fd03ff6d962eb9a835","source":{"kind":"arxiv","id":"1909.06814","version":4},"attestation_state":"computed","paper":{"title":"Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Leshem Choshen, Omri Abend","submitted_at":"2019-09-15T15:21:20Z","abstract_excerpt":"We show that the state of the art Transformer Machine Translation (MT) model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We, therefore, propose an automatic approach for extracting challenge sets replete with long-distance dependencies and argue that evaluation using this methodology provides a complementary perspective on system 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":"1909.06814","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-15T15:21:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35882e74252b59ad866e5cb85428d4e7686f301eac9b66d86113b0ee040d2fe5","abstract_canon_sha256":"0be42aed121270db152c39731d69417f7c86ad66b3320cdfbfa116d3aa81308b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:08.114743Z","signature_b64":"Te9MMHVoWltVBd8Wo/Aa5LHIIej7S0P80E65VxrPqC5a/N/SMVhf29sWTI2bh0PAgmTzvObS2ry/83v3qzbXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f1126e3ecdb87b9f0bbec40628ffe8540c7fbe1cd9554fd03ff6d962eb9a835","last_reissued_at":"2026-07-05T00:07:08.114352Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:08.114352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Leshem Choshen, Omri Abend","submitted_at":"2019-09-15T15:21:20Z","abstract_excerpt":"We show that the state of the art Transformer Machine Translation (MT) model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We, therefore, propose an automatic approach for extracting challenge sets replete with long-distance dependencies and argue that evaluation using this methodology provides a complementary perspective on system p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06814","kind":"arxiv","version":4},"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/1909.06814/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":"1909.06814","created_at":"2026-07-05T00:07:08.114408+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.06814v4","created_at":"2026-07-05T00:07:08.114408+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06814","created_at":"2026-07-05T00:07:08.114408+00:00"},{"alias_kind":"pith_short_12","alias_value":"H4ISNY7M3OD3","created_at":"2026-07-05T00:07:08.114408+00:00"},{"alias_kind":"pith_short_16","alias_value":"H4ISNY7M3OD3T4F3","created_at":"2026-07-05T00:07:08.114408+00:00"},{"alias_kind":"pith_short_8","alias_value":"H4ISNY7M","created_at":"2026-07-05T00:07:08.114408+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/H4ISNY7M3OD3T4F35RAGFD76QV","json":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV.json","graph_json":"https://pith.science/api/pith-number/H4ISNY7M3OD3T4F35RAGFD76QV/graph.json","events_json":"https://pith.science/api/pith-number/H4ISNY7M3OD3T4F35RAGFD76QV/events.json","paper":"https://pith.science/paper/H4ISNY7M"},"agent_actions":{"view_html":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV","download_json":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV.json","view_paper":"https://pith.science/paper/H4ISNY7M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.06814&json=true","fetch_graph":"https://pith.science/api/pith-number/H4ISNY7M3OD3T4F35RAGFD76QV/graph.json","fetch_events":"https://pith.science/api/pith-number/H4ISNY7M3OD3T4F35RAGFD76QV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV/action/storage_attestation","attest_author":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV/action/author_attestation","sign_citation":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV/action/citation_signature","submit_replication":"https://pith.science/pith/H4ISNY7M3OD3T4F35RAGFD76QV/action/replication_record"}},"created_at":"2026-07-05T00:07:08.114408+00:00","updated_at":"2026-07-05T00:07:08.114408+00:00"}