{"as_of":"2026-08-12T10:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b36949dc6aeca4750bf2b45e4234e44b6615d72ed7fb136ca86adbaec1d0a175","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:40:20.086547Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T00:31:01.503923Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T00:36:39.457285Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"cited_work":{"arxiv_id":"2412.00208","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.00208","snapshot_observed_at":"2026-07-10T00:36:39.457285Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","venue":"cs.CL","work_id":"4bf3962b-9f1d-41d7-9389-07eaa5d586b0","year":2024},"citing_paper":{"arxiv_id":"2607.08080","last_updated":"2026-07-09T03:20:54Z","snapshot_observed_at":"2026-08-02T12:33:36.573753Z","submitted_at":"2026-07-09T03:20:54Z","title":"MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-07-10T00:31:01.503923Z"},"links":{"cited_paper":"/paper/2412.00208","citing_paper":"/paper/2607.08080"},"observation_digest":"sha256:3726c3eb93c7786cdad4b33b97b2156d5c13dd928cca91838c595487db31f64b","observation_id":"3f3d7f8b-8e5b-4d2d-bedd-873a2dde5872","resolution":{"observed_at":"2026-07-10T00:36:39.459046Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.00208/citation-record","integrity":"/paper/2412.00208/integrity","json":"/paper/2412.00208/citation-record.json","paper":"/paper/2412.00208"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.620257Z","title":null,"venue":null,"work_id":"44f2032f-400b-4409-a88f-c6880b882542","year":1973},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.926660Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:edb3b5f93592c7c9c33d65a642ba109a6197ec3e3aab86f1a26ea305c4ca12f1","observation_id":"7d32f5b0-44c6-4585-b1db-966fb2852fa7","resolution":{"observed_at":"2026-08-12T05:40:20.624412Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.608793Z","title":null,"venue":null,"work_id":"06e3ce81-a427-4f4f-91b3-2d2ab3a0a919","year":2021},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.931564Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:d994a32f7cfc9d1495872889879e4740ab34420d81e2632b7c6e22a67d3acccd","observation_id":"b5fddbc9-b0cb-47e8-8699-dd58c28701df","resolution":{"observed_at":"2026-08-12T05:40:20.612680Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.596570Z","title":null,"venue":null,"work_id":"e2919e8b-22b0-44cd-8acf-895a461cf46b","year":2017},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.935599Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:57625e206f2aeb6cd280494755ac5bfaa7ed1093f329dd058d09598e97d2d4ae","observation_id":"6cae4ea8-1f71-4824-9025-66994ad41276","resolution":{"observed_at":"2026-08-12T05:40:20.600911Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.583841Z","title":null,"venue":null,"work_id":"1bae33af-d4ba-43f8-84c9-c0dcc49a6241","year":2010},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.939470Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:331c700d11e69ac3674ed487ee4d4c3d4ecf1890834c4b2a223d61deda57b312","observation_id":"5ddbde6d-fc64-4739-8ab2-dd7f0992b614","resolution":{"observed_at":"2026-08-12T05:40:20.587903Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19260","last_updated":"2024-04-30T04:53:59Z","snapshot_observed_at":"2026-07-06T18:07:27.432252Z","submitted_at":"2024-04-30T04:53:59Z","title":"Aspect and Opinion Term Extraction Using Graph Attention Network","version":1},"cited_work":{"arxiv_id":"2404.19260","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.19260","snapshot_observed_at":"2026-08-12T05:40:20.403447Z","title":"Aspect and Opinion Term Extraction Using Graph Attention Network","venue":"cs.CL","work_id":"283f52c2-c034-4bf7-af29-ff494420a530","year":2024},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.943163Z"},"links":{"cited_paper":"/paper/2404.19260","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:75e612bd29805574437eb4c03ce4ea402c4e5fdcc4cd8cdce6b18abfbca4be15","observation_id":"7fdd25cc-59a9-447c-91f7-3bf33cf1db7c","resolution":{"observed_at":"2026-08-12T05:40:20.407850Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:19.947576Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.947576Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:ecb646be2b4e6dc196accca3471ca53b1512e032061019c0cd5eca7ae4ff700d","observation_id":"4ab29107-9eb5-4348-8b80-3b6aef4cb4bd","resolution":{"observed_at":"2026-08-12T05:40:19.947576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07568","last_updated":"2023-02-17T21:53:23Z","snapshot_observed_at":"2026-08-10T06:31:03.163535Z","submitted_at":"2022-06-15T14:34:15Z","title":"Contrastive Learning as Goal-Conditioned Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07568","snapshot_observed_at":"2026-08-12T05:40:19.951671Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.951671Z"},"links":{"cited_paper":"/paper/2206.07568","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:fca17b93865bcaf066be106301261580784e54aaef8cddd62523f5309c89e515","observation_id":"ef8a096b-2410-4735-9706-57621830ca3e","resolution":{"observed_at":"2026-08-12T05:40:19.951671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2305.05311","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.372397Z","title":null,"venue":null,"work_id":"376805aa-8622-48ab-b40b-b2783279d50c","year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.955665Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:1d964ec4fb140c03420ba5995904fb86d64505eeb50ca7d052fb5207e9facbc3","observation_id":"529f001f-6cd6-4602-8d66-4364555dc20e","resolution":{"observed_at":"2026-08-12T05:40:20.378412Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:19.959287Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.959287Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:6abe74990ff6647c3d5ba018022d8c74afbe162c5379761c10fe6c45cc396958","observation_id":"a8441b1a-2d3a-4d93-94a9-38b5d52818b5","resolution":{"observed_at":"2026-08-12T05:40:19.959287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00584","last_updated":"2020-11-01T18:25:15Z","snapshot_observed_at":"2026-08-12T02:00:57.516444Z","submitted_at":"2020-11-01T18:25:15Z","title":"A Unifying Theory of Transition-based and Sequence Labeling Parsing","version":1},"cited_work":{"arxiv_id":"2011.00584","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.00584","snapshot_observed_at":"2026-08-12T05:40:20.292846Z","title":"A Unifying Theory of Transition-based and Sequence Labeling Parsing","venue":"cs.CL","work_id":"5a78c55d-1cdc-4e50-89a4-76a88b718edd","year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.963147Z"},"links":{"cited_paper":"/paper/2011.00584","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:b8a5565242eb4a1b49a79bdbb8425b2cb58d34b19aac3070a22ae04fc0b6a7a8","observation_id":"31dd76f1-2533-49cb-9f55-a4131e5c276f","resolution":{"observed_at":"2026-08-12T05:40:20.297308Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:19.967030Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.967030Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:553c98e9bd6b6c35039c6a723bbb0614659f7b0765970fbf2927b6e8c0fb2ebf","observation_id":"858c047a-4305-4d86-a575-4ae911e54ecc","resolution":{"observed_at":"2026-08-12T05:40:19.967030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.559075Z","title":null,"venue":null,"work_id":"1d3d3592-5469-41c0-93f1-6f580242e9d4","year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.970941Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:4f7a7dca3f3bae79e27b8465300008e1ea7b7de1721714df9b75d9738f1d6d32","observation_id":"28bd3a8c-c98e-4e00-8a54-f696fc546052","resolution":{"observed_at":"2026-08-12T05:40:20.562801Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01308","last_updated":"2022-05-03T04:56:45Z","snapshot_observed_at":"2026-08-09T17:50:54.689520Z","submitted_at":"2022-05-03T04:56:45Z","title":"Contrastive Learning for Prompt-Based Few-Shot Language Learners","version":1},"cited_work":{"arxiv_id":"2205.01308","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.01308","snapshot_observed_at":"2026-08-12T05:40:20.275571Z","title":"Contrastive Learning for Prompt-Based Few-Shot Language Learners","venue":"cs.CL","work_id":"ac96391d-52d3-486b-852e-f19c4307c6b4","year":2022},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.974673Z"},"links":{"cited_paper":"/paper/2205.01308","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:ce339389f6fbca637835f0e2d8cc4c375d94c52309a042ed69da924833970697","observation_id":"898e5d5f-cc70-461a-8e71-a8afd378535a","resolution":{"observed_at":"2026-08-12T05:40:20.279954Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08373","last_updated":"2023-06-14T09:04:14Z","snapshot_observed_at":"2026-08-05T07:04:31.622737Z","submitted_at":"2023-06-14T09:04:14Z","title":"A semantically enhanced dual encoder for aspect sentiment triplet extraction","version":1},"cited_work":{"arxiv_id":"2306.08373","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.08373","snapshot_observed_at":"2026-08-12T05:40:20.259327Z","title":"A semantically enhanced dual encoder for aspect sentiment triplet extraction","venue":"cs.CL","work_id":"82839d93-dc06-417b-8ebe-2678dda10e63","year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.978871Z"},"links":{"cited_paper":"/paper/2306.08373","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:74e5042a2ef45fe943af16bbeb53bea1e037d6e8f3f1ddfe9c9b3f7fcf99b99c","observation_id":"58b60295-28c6-4827-8f7a-be539e9d3377","resolution":{"observed_at":"2026-08-12T05:40:20.264063Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.546730Z","title":null,"venue":null,"work_id":"08ce5848-fe33-4724-a5fa-45f91aedc24a","year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.983305Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:eacb0a3b825e1e9c0ca0492cf4ae875fc0098ca38301975ff534ad335f33d4c8","observation_id":"17165031-c78c-401f-9b7a-41a1f42debb3","resolution":{"observed_at":"2026-08-12T05:40:20.551579Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:19.987127Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.987127Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:d78aee2639d9cbafb4093c57d3ab627d4277604a0b8807cec163b8c6912175e3","observation_id":"4fa8392e-7cbe-407f-8571-5dfcdf184157","resolution":{"observed_at":"2026-08-12T05:40:19.987127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.526667Z","title":null,"venue":null,"work_id":"1ee62527-f6de-423d-83d4-34d12d0ff825","year":2022},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.990985Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:146ae50a3246a7653734189086e63a3ba5782a1396f86bb364c074ebac1ee9ac","observation_id":"dbc9536a-82a8-4603-ac31-f318cf1a089f","resolution":{"observed_at":"2026-08-12T05:40:20.531180Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-12T05:40:19.995316Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.995316Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:44b2edf72aa187050be7691600a6a0e2ed1435422e35bb5b07943f29d9a270d3","observation_id":"421f9129-9c20-47cc-83d1-1b03d2c30e07","resolution":{"observed_at":"2026-08-12T05:40:19.995316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.513987Z","title":null,"venue":null,"work_id":"7a27cc95-b466-4433-85c6-680badd7e4ea","year":2021},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:19.999673Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:4ffb811153da77a44c33ee8a480900936849a87783b553186122c7281ae7afde","observation_id":"947b2a48-9e5e-4536-930e-0774f3f811b4","resolution":{"observed_at":"2026-08-12T05:40:20.517991Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.501162Z","title":null,"venue":null,"work_id":"ebd72df8-bdab-45fc-bace-b1e29ed28997","year":2003},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.004578Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:c4aae6985af15b5350e5f6cf0b84d729039d789eb6fc0b06324dfa61935d4256","observation_id":"f8abdd7b-7a1f-4a1f-ae6e-0e9661df7279","resolution":{"observed_at":"2026-08-12T05:40:20.505361Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.008344Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.008344Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:760c34f21bc8848245c7fe38d0a9eb2bfa984fee1bb842c9a4594c929d3393bd","observation_id":"203ebf12-f8b1-49c4-838f-3fb5842422d4","resolution":{"observed_at":"2026-08-12T05:40:20.008344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.012389Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.012389Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:cd8298257abda5f3e8a355e6660350ab24db06d6c2c341f82441dc2a1c79bb91","observation_id":"f0869d38-cf27-4365-9a65-f047b2525adb","resolution":{"observed_at":"2026-08-12T05:40:20.012389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.017248Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.017248Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:ad1e34c0b18f6a153e55e2ac0a14d9a73bb9b1b54c27f51f14382b23526cabf0","observation_id":"73f291f1-1fc0-4b94-b66c-2ef797cc2ae2","resolution":{"observed_at":"2026-08-12T05:40:20.017248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.022251Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.022251Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:e6debe60c989c77623d240b8d010751c824ad4d2522f1f4c46471fa9414a495c","observation_id":"e22c8489-d007-4e9f-8f33-e1402c9809c0","resolution":{"observed_at":"2026-08-12T05:40:20.022251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11526","last_updated":"2023-10-10T14:37:45Z","snapshot_observed_at":"2026-08-12T03:34:08.489372Z","submitted_at":"2023-06-20T13:28:27Z","title":"Understanding Contrastive Learning Through the Lens of Margins","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11526","snapshot_observed_at":"2026-08-12T05:40:20.026410Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.026410Z"},"links":{"cited_paper":"/paper/2306.11526","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:a9ff64fe9f1b0709ba2c838a650bc24bf8c04b57ee68d2b3027e8e02983db869","observation_id":"c57d4685-c239-450b-8715-00d9cfb75672","resolution":{"observed_at":"2026-08-12T05:40:20.026410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.488332Z","title":null,"venue":null,"work_id":"92831d79-f14f-47d8-b70e-ed9123c6577f","year":2024},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.031344Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:59e44ab65d99e1d3207a9b664479912b044cf864c544b78ffa3c480395f82ccc","observation_id":"948b9c01-a321-4bc5-afcc-58b730782a66","resolution":{"observed_at":"2026-08-12T05:40:20.492329Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.474757Z","title":null,"venue":null,"work_id":"18306602-bdbd-43fd-b3dd-6ebd06365fa9","year":2023},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.035390Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:5782b112be7f73a3748f59ca7cd601a5cd77f97131bca8df093118839cdfff71","observation_id":"8383e731-41a7-40e6-955d-bd838209bdf1","resolution":{"observed_at":"2026-08-12T05:40:20.480263Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.463801Z","title":null,"venue":null,"work_id":"b89cf80e-6488-4fa7-8547-d039d5986ad4","year":2017},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.039262Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:c0f71d55b4ab2cbfeb9d7c1ccdd05e22f004bc169366991c562631475e46b625","observation_id":"692f9bdb-9ae9-42d8-85bd-4ce51baf1b39","resolution":{"observed_at":"2026-08-12T05:40:20.467634Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04640","last_updated":"2020-11-03T15:55:08Z","snapshot_observed_at":"2026-08-10T02:18:36.374670Z","submitted_at":"2020-10-09T15:33:50Z","title":"Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04640","snapshot_observed_at":"2026-08-12T05:40:20.043487Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.043487Z"},"links":{"cited_paper":"/paper/2010.04640","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:810101a6e94904e48439d1c7140a45ecd6addcc610af30054e1104e3f66c7a7f","observation_id":"bf83451d-08c5-49be-ad19-cd392754ea03","resolution":{"observed_at":"2026-08-12T05:40:20.043487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02609","last_updated":"2021-03-09T15:38:12Z","snapshot_observed_at":"2026-08-10T22:21:23.603327Z","submitted_at":"2020-10-06T10:40:34Z","title":"Position-Aware Tagging for Aspect Sentiment Triplet Extraction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02609","snapshot_observed_at":"2026-08-12T05:40:20.051484Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.051484Z"},"links":{"cited_paper":"/paper/2010.02609","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:ff6be8fc78c66f4d71cc8a8d11b191371421de0bc42371e8ad7ae65e6453f647","observation_id":"4687580e-88a8-4aca-b2f5-04d9e7f307c4","resolution":{"observed_at":"2026-08-12T05:40:20.051484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.acl-long.188","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.129616Z","title":null,"venue":null,"work_id":"912e504f-c7a8-46e9-85ab-92edc7ed0b6e","year":2021},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.055536Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:10c34bb87cef1e76c3802bac5e874b52258bbb342f0b1f190a53199129534a9e","observation_id":"d886985a-4ed5-4f88-a6d1-a8970235af37","resolution":{"observed_at":"2026-08-12T05:40:20.133822Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.14568","last_updated":"2020-10-27T19:19:38Z","snapshot_observed_at":"2026-08-12T01:16:07.763976Z","submitted_at":"2020-10-27T19:19:38Z","title":"Strongly Incremental Constituency Parsing with Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2010.14568","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.14568","snapshot_observed_at":"2026-08-12T05:40:20.196978Z","title":"Strongly Incremental Constituency Parsing with Graph Neural Networks","venue":"cs.CL","work_id":"5e94c295-3bdb-45a5-ac5c-3e81971d4874","year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.059398Z"},"links":{"cited_paper":"/paper/2010.14568","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:250345f467125c62f517736f17e321ff7b7d12baa24f90e83084659c01edc3d3","observation_id":"0ab06f00-2f58-44ab-b08f-a4466940aced","resolution":{"observed_at":"2026-08-12T05:40:20.202291Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.063729Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.063729Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:16e53f3aa5d697a2f50df452f01bc99d664c4126aebfd5f28b9f9fae1b256660","observation_id":"565842ab-2017-4cc7-acf8-87af5c4cf557","resolution":{"observed_at":"2026-08-12T05:40:20.063729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.443663Z","title":null,"venue":null,"work_id":"38e611b8-e68c-4f14-aaec-64a78ae0bf4b","year":2016},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.067392Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:219ceae55ce0563e452ec3286bb3ddd5e26049ef166765f06643d35a63064398","observation_id":"a07be604-2421-40af-807d-dbb871f78dc8","resolution":{"observed_at":"2026-08-12T05:40:20.448334Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2020.acl-main.296","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.115139Z","title":null,"venue":null,"work_id":"f54a00f1-76b7-4df9-bfb8-abbcfded70c1","year":2020},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.071026Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:406be9eae5c2e25cf17840e40db005fd092a13415c86911b45fa4092c3e1a0ab","observation_id":"b3a961ae-70bc-4ea7-aefb-df428dd5d8bc","resolution":{"observed_at":"2026-08-12T05:40:20.121133Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.15534","last_updated":"2021-10-29T04:36:31Z","snapshot_observed_at":"2026-07-06T12:03:17.964795Z","submitted_at":"2021-10-29T04:36:31Z","title":"Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.15534","snapshot_observed_at":"2026-08-12T05:40:20.074771Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.074771Z"},"links":{"cited_paper":"/paper/2110.15534","citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:d182cb8e7cbabd6e8ddb0e1e45d256387a649c2180ba85e2d97f4fb94dc5685b","observation_id":"032d2198-94e9-4637-b2ce-4860ba260e78","resolution":{"observed_at":"2026-08-12T05:40:20.074771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.430571Z","title":null,"venue":null,"work_id":"d44678b2-4f6a-4516-be2f-39738016ba7f","year":2024},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.078666Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:76c4190e3fbf5473be9798e316fdeafb757345c3b9eae56dbf06e1aac148d323","observation_id":"e272d3ba-9adc-4e13-b46a-c1170d48ecc5","resolution":{"observed_at":"2026-08-12T05:40:20.434732Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.082368Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.082368Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:55c9fabd7e52151d135ad5f0a368e70c76c51af387fb2f5f8502249410509c0c","observation_id":"ef8e7a66-d53c-4845-a407-2ea4d19d2859","resolution":{"observed_at":"2026-08-12T05:40:20.082368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:40:20.086547Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-12T05:40:20.086547Z"},"links":{"citing_paper":"/paper/2412.00208"},"observation_digest":"sha256:e07831eeba6fa76533506e9a7f047d32a00f217667106f4dd0847aa6ab98f90a","observation_id":"bcdde1e8-4d58-4a20-a1f5-c9d60981596e","resolution":{"observed_at":"2026-08-12T05:40:20.086547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.00208","last_updated":"2025-02-07T12:12:16Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T05:34:57.236293Z","submitted_at":"2024-11-29T19:10:41Z","title":"Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":8,"verified_fuzzy":0},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2412.00208."}