{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:LKE4GYXFI356ZDDWI3BG7TTUCF","short_pith_number":"pith:LKE4GYXF","canonical_record":{"source":{"id":"2208.08678","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T07:25:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0f8469978824467225ff541b4477d67781b9574771416545d29da8982442134f","abstract_canon_sha256":"f27ce5541a2e547111f072f548f94aa0f1c369d423a92e168482447960d2789c"},"schema_version":"1.0"},"canonical_sha256":"5a89c362e546fbec8c7646c26fce74117815db9305c3aa500efff097f07f2d72","source":{"kind":"arxiv","id":"2208.08678","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.08678","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"arxiv_version","alias_value":"2208.08678v1","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.08678","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_12","alias_value":"LKE4GYXFI356","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_16","alias_value":"LKE4GYXFI356ZDDW","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_8","alias_value":"LKE4GYXF","created_at":"2026-07-05T04:49:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:LKE4GYXFI356ZDDWI3BG7TTUCF","target":"record","payload":{"canonical_record":{"source":{"id":"2208.08678","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T07:25:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0f8469978824467225ff541b4477d67781b9574771416545d29da8982442134f","abstract_canon_sha256":"f27ce5541a2e547111f072f548f94aa0f1c369d423a92e168482447960d2789c"},"schema_version":"1.0"},"canonical_sha256":"5a89c362e546fbec8c7646c26fce74117815db9305c3aa500efff097f07f2d72","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:36.594790Z","signature_b64":"Fcm2fz38KAdLp0Q16PcxPVOWLLObwcWFDqoNmJ4CLAehCxuSXf9zUtlt7Ha/bGF5n5CqqlJAfdSDAQBuc0/7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a89c362e546fbec8c7646c26fce74117815db9305c3aa500efff097f07f2d72","last_reissued_at":"2026-07-05T04:49:36.594388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:36.594388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.08678","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:49:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pXw3yYw759xaHKlqEGXVmvw1jv0QPmqrBC4dc75nYS+qTllitSgBk0WXy7iE0a0FTHm3TctZYzCirrJT5LfdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:08.497972Z"},"content_sha256":"58ee973342437f1d980f363acaf15ce4b630504008fe7ddb351bb522ab280d9e","schema_version":"1.0","event_id":"sha256:58ee973342437f1d980f363acaf15ce4b630504008fe7ddb351bb522ab280d9e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:LKE4GYXFI356ZDDWI3BG7TTUCF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mere Contrastive Learning for Cross-Domain Sentiment Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fang Guo, Yue Zhang, Yun Luo, Zihan Liu","submitted_at":"2022-08-18T07:25:55Z","abstract_excerpt":"Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data. Previous studies are mostly cross-entropy-based methods for the task, which suffer from instability and poor generalization. In this paper, we explore contrastive learning on the cross-domain sentiment analysis task. We propose a modified contrastive objective with in-batch negative samples so that the sentence representations from the same class will be pushed close while those from the different classes become furt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.08678","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/2208.08678/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:49:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ywCzIHJ5huE+G4T8wDsHxJHYxd6I/xbt0bTOGOp8fwrzdD7FSarp4IAjBpKdqUtPIX2mvJkZMpCPIWsTxJfYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:08.498874Z"},"content_sha256":"b5d195c4875b3c585b4f2b1460144060880e5131600074325bd5ca611e4dec94","schema_version":"1.0","event_id":"sha256:b5d195c4875b3c585b4f2b1460144060880e5131600074325bd5ca611e4dec94"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/bundle.json","state_url":"https://pith.science/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T07:22:08Z","links":{"resolver":"https://pith.science/pith/LKE4GYXFI356ZDDWI3BG7TTUCF","bundle":"https://pith.science/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/bundle.json","state":"https://pith.science/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LKE4GYXFI356ZDDWI3BG7TTUCF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LKE4GYXFI356ZDDWI3BG7TTUCF","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":"f27ce5541a2e547111f072f548f94aa0f1c369d423a92e168482447960d2789c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T07:25:55Z","title_canon_sha256":"0f8469978824467225ff541b4477d67781b9574771416545d29da8982442134f"},"schema_version":"1.0","source":{"id":"2208.08678","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.08678","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"arxiv_version","alias_value":"2208.08678v1","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.08678","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_12","alias_value":"LKE4GYXFI356","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_16","alias_value":"LKE4GYXFI356ZDDW","created_at":"2026-07-05T04:49:36Z"},{"alias_kind":"pith_short_8","alias_value":"LKE4GYXF","created_at":"2026-07-05T04:49:36Z"}],"graph_snapshots":[{"event_id":"sha256:b5d195c4875b3c585b4f2b1460144060880e5131600074325bd5ca611e4dec94","target":"graph","created_at":"2026-07-05T04:49:36Z","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/2208.08678/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data. Previous studies are mostly cross-entropy-based methods for the task, which suffer from instability and poor generalization. In this paper, we explore contrastive learning on the cross-domain sentiment analysis task. We propose a modified contrastive objective with in-batch negative samples so that the sentence representations from the same class will be pushed close while those from the different classes become furt","authors_text":"Fang Guo, Yue Zhang, Yun Luo, Zihan Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T07:25:55Z","title":"Mere Contrastive Learning for Cross-Domain Sentiment Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.08678","kind":"arxiv","version":1},"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":"sha256:58ee973342437f1d980f363acaf15ce4b630504008fe7ddb351bb522ab280d9e","target":"record","created_at":"2026-07-05T04:49:36Z","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":"f27ce5541a2e547111f072f548f94aa0f1c369d423a92e168482447960d2789c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-18T07:25:55Z","title_canon_sha256":"0f8469978824467225ff541b4477d67781b9574771416545d29da8982442134f"},"schema_version":"1.0","source":{"id":"2208.08678","kind":"arxiv","version":1}},"canonical_sha256":"5a89c362e546fbec8c7646c26fce74117815db9305c3aa500efff097f07f2d72","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a89c362e546fbec8c7646c26fce74117815db9305c3aa500efff097f07f2d72","first_computed_at":"2026-07-05T04:49:36.594388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:36.594388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fcm2fz38KAdLp0Q16PcxPVOWLLObwcWFDqoNmJ4CLAehCxuSXf9zUtlt7Ha/bGF5n5CqqlJAfdSDAQBuc0/7Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:36.594790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.08678","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58ee973342437f1d980f363acaf15ce4b630504008fe7ddb351bb522ab280d9e","sha256:b5d195c4875b3c585b4f2b1460144060880e5131600074325bd5ca611e4dec94"],"state_sha256":"ef18beb2a6a3ba6b03fd37de92c4a6294f2a661da111a9066d81219809a15c35"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HR0z/ejLnGK6ibaSlSh4fMLpLtQ6i8bIZtyQZfvKjgh5rMgT9MopFPADVd+gs8Ci7m4/g3qxSCnegkICEHEbBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:22:08.508141Z","bundle_sha256":"593095d548916f6f7b4116a69f6d4ea5d9da3c94663bd49712a84d81f771a44c"}}