{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KF6ULYBZVFMTFWPQW5OGX3K5SN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6b47f32f9a3abddeaf5d5f4393be37b00cc9bcbac8d5ab45c568334faf3158c2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-09-08T18:31:42Z","title_canon_sha256":"0829266331bcb73cca4903873d32f6fe17831a62f230e40bc5eca8be725f6512"},"schema_version":"1.0","source":{"id":"1909.03526","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.03526","created_at":"2026-07-05T00:16:05Z"},{"alias_kind":"arxiv_version","alias_value":"1909.03526v3","created_at":"2026-07-05T00:16:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03526","created_at":"2026-07-05T00:16:05Z"},{"alias_kind":"pith_short_12","alias_value":"KF6ULYBZVFMT","created_at":"2026-07-05T00:16:05Z"},{"alias_kind":"pith_short_16","alias_value":"KF6ULYBZVFMTFWPQ","created_at":"2026-07-05T00:16:05Z"},{"alias_kind":"pith_short_8","alias_value":"KF6ULYBZ","created_at":"2026-07-05T00:16:05Z"}],"graph_snapshots":[{"event_id":"sha256:dd9703c86870b5123de2cac528dc1fea445d121c2dcf577a4d6e8a60b0545f0a","target":"graph","created_at":"2026-07-05T00:16:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.03526/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Supervised deep learning requires large amounts of training data. In the context of the FIRE2019 Arabic irony detection shared task (IDAT@FIRE2019), we show how we mitigate this need by fine-tuning the pre-trained bidirectional encoders from transformers (BERT) on gold data in a multi-task setting. We further improve our models by by further pre-training BERT on `in-domain' data, thus alleviating an issue of dialect mismatch in the Google-released BERT model. Our best model acquires 82.4 macro F1 score, and has the unique advantage of being feature-engineering free (i.e., based exclusively on ","authors_text":"Chiyu Zhang, Muhammad Abdul-Mageed","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-09-08T18:31:42Z","title":"Multi-Task Bidirectional Transformer Representations for Irony Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03526","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9cea84196d5d2e9dd84b6dc4a4bb4aa84fd6e594fd3fc76bb8f10ee843a94f6c","target":"record","created_at":"2026-07-05T00:16:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6b47f32f9a3abddeaf5d5f4393be37b00cc9bcbac8d5ab45c568334faf3158c2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2019-09-08T18:31:42Z","title_canon_sha256":"0829266331bcb73cca4903873d32f6fe17831a62f230e40bc5eca8be725f6512"},"schema_version":"1.0","source":{"id":"1909.03526","kind":"arxiv","version":3}},"canonical_sha256":"517d45e039a95932d9f0b75c6bed5d937a49001770e3e421734d699666c9a095","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"517d45e039a95932d9f0b75c6bed5d937a49001770e3e421734d699666c9a095","first_computed_at":"2026-07-05T00:16:05.457507Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:16:05.457507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B8ljoSBn6+bThDNCYbLlov7V3oe6/OWmKYzgEdrpy4tH08mO3YNGOzNNzSFg7Io+GtnUhcqKse9yS1H5nl1kBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:16:05.457996Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.03526","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9cea84196d5d2e9dd84b6dc4a4bb4aa84fd6e594fd3fc76bb8f10ee843a94f6c","sha256:dd9703c86870b5123de2cac528dc1fea445d121c2dcf577a4d6e8a60b0545f0a"],"state_sha256":"6b0d31d1c0bcfd3e87f07071ce1100f21d52dbc5f7496d210e36482cefb012fb"}