{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4J4AMAAKT4YGM3BOJXH53V5ANV","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":"6cdc6ab95592a5e9accff53a257118c5897be8e40eac86f95f5c04e64d149c8c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T07:01:34Z","title_canon_sha256":"22ea292be302b0313685519f18984a4c52328189bc46b75d8aec6858e213e89e"},"schema_version":"1.0","source":{"id":"2506.00863","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00863","created_at":"2026-07-05T12:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00863v2","created_at":"2026-07-05T12:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00863","created_at":"2026-07-05T12:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"4J4AMAAKT4YG","created_at":"2026-07-05T12:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"4J4AMAAKT4YGM3BO","created_at":"2026-07-05T12:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"4J4AMAAK","created_at":"2026-07-05T12:01:23Z"}],"graph_snapshots":[{"event_id":"sha256:db6c9c36d535f8ea67b5a338c2dfe3bdc1fe2ee447949da106c99d329294c5c5","target":"graph","created_at":"2026-07-05T12:01:23Z","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/2506.00863/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Emotion recognition in low-resource languages like Marathi remains challenging due to limited annotated data. We present L3Cube-MahaEmotions, a high-quality Marathi emotion recognition dataset with 11 fine-grained emotion labels. The training data is synthetically annotated using large language models (LLMs), while the validation and test sets are manually labeled to serve as a reliable gold-standard benchmark. Building on the MahaSent dataset, we apply the Chain-of-Translation (CoTR) prompting technique, where Marathi sentences are translated into English and emotion labeled via a single prom","authors_text":"Nidhi Kowtal, Raviraj Joshi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T07:01:34Z","title":"L3Cube-MahaEmotions: A Marathi Emotion Recognition Dataset with Synthetic Annotations using CoTR prompting and Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00863","kind":"arxiv","version":2},"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":"sha256:692680a8e461c838abcecb452b2c43904b17f9587dafc65756f941a2428664b9","target":"record","created_at":"2026-07-05T12:01:23Z","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":"6cdc6ab95592a5e9accff53a257118c5897be8e40eac86f95f5c04e64d149c8c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T07:01:34Z","title_canon_sha256":"22ea292be302b0313685519f18984a4c52328189bc46b75d8aec6858e213e89e"},"schema_version":"1.0","source":{"id":"2506.00863","kind":"arxiv","version":2}},"canonical_sha256":"e27806000a9f30666c2e4dcfddd7a06d72575fc20767863df4130a203fc12c30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e27806000a9f30666c2e4dcfddd7a06d72575fc20767863df4130a203fc12c30","first_computed_at":"2026-07-05T12:01:23.040343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:01:23.040343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SWyk7yruXHYxnqsGl3dFBQNWFwQs1nS73ytGy8tzLyWJ7ueIcaYTWOQCcO33h/b/Ha5NQCm8OqPZvb2JTN0mCA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:01:23.040761Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.00863","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:692680a8e461c838abcecb452b2c43904b17f9587dafc65756f941a2428664b9","sha256:db6c9c36d535f8ea67b5a338c2dfe3bdc1fe2ee447949da106c99d329294c5c5"],"state_sha256":"7dd53e0df2711fa7be5dfeaf7d8fad93cb0f4e62617e139981220d0de8b65fb8"}