Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.15534.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T05:40:20.074771Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T04:33:37.658431Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 032d2198-94e9-4637-b2ce-4860ba260e78 · inbound
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction cites this paper.
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:40:20.074771Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77e904a3-a942-4911-9645-934ab09d2316 · inbound
A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning cites this paper.
A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T04:33:37.720741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.