{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:C6LNOQG77E2SPH4TDAKERRRITY","short_pith_number":"pith:C6LNOQG7","schema_version":"1.0","canonical_sha256":"1796d740dff935279f93181448c6289e3c54a91956f483b5981f9c02a02d775a","source":{"kind":"arxiv","id":"2305.11979","version":1},"attestation_state":"computed","paper":{"title":"A Weak Supervision Approach for Few-Shot Aspect Based Sentiment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Giovanni Paolini, Kishaloy Halder, Miguel Ballesteros, Neha Anna John, Robert Vacareanu, Shuai Wang, Siddharth Varia, Smaranda Muresan","submitted_at":"2023-05-19T19:53:54Z","abstract_excerpt":"We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84% absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., w"},"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":"2305.11979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T19:53:54Z","cross_cats_sorted":[],"title_canon_sha256":"0962d08c39a64bcdfe46d9c48f6a5fc6f20a987747ce45612953a77ca67522ab","abstract_canon_sha256":"5a77dcd70a4c37713302eb843df62c4d7fbbdc20aca49ef2cbfed729dd98320b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:05.311338Z","signature_b64":"p2kZ1YM2QVcSDAEDWNPXy9kdps+zyIPW5aCUF6MTS7KVOecTUelGA9oxWnuLjZch6cmYvnt225QolGmWM/myDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1796d740dff935279f93181448c6289e3c54a91956f483b5981f9c02a02d775a","last_reissued_at":"2026-07-05T06:12:05.310757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:05.310757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Weak Supervision Approach for Few-Shot Aspect Based Sentiment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Giovanni Paolini, Kishaloy Halder, Miguel Ballesteros, Neha Anna John, Robert Vacareanu, Shuai Wang, Siddharth Varia, Smaranda Muresan","submitted_at":"2023-05-19T19:53:54Z","abstract_excerpt":"We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84% absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11979","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/2305.11979/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":"2305.11979","created_at":"2026-07-05T06:12:05.310822+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.11979v1","created_at":"2026-07-05T06:12:05.310822+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11979","created_at":"2026-07-05T06:12:05.310822+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6LNOQG77E2S","created_at":"2026-07-05T06:12:05.310822+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6LNOQG77E2SPH4T","created_at":"2026-07-05T06:12:05.310822+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6LNOQG7","created_at":"2026-07-05T06:12:05.310822+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/C6LNOQG77E2SPH4TDAKERRRITY","json":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY.json","graph_json":"https://pith.science/api/pith-number/C6LNOQG77E2SPH4TDAKERRRITY/graph.json","events_json":"https://pith.science/api/pith-number/C6LNOQG77E2SPH4TDAKERRRITY/events.json","paper":"https://pith.science/paper/C6LNOQG7"},"agent_actions":{"view_html":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY","download_json":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY.json","view_paper":"https://pith.science/paper/C6LNOQG7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.11979&json=true","fetch_graph":"https://pith.science/api/pith-number/C6LNOQG77E2SPH4TDAKERRRITY/graph.json","fetch_events":"https://pith.science/api/pith-number/C6LNOQG77E2SPH4TDAKERRRITY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY/action/storage_attestation","attest_author":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY/action/author_attestation","sign_citation":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY/action/citation_signature","submit_replication":"https://pith.science/pith/C6LNOQG77E2SPH4TDAKERRRITY/action/replication_record"}},"created_at":"2026-07-05T06:12:05.310822+00:00","updated_at":"2026-07-05T06:12:05.310822+00:00"}