Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
End-to-End Open-Domain Question Answering with BERTserini
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1902.01718.
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 2026-08-14T12:58:19.090162Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T18:00:50.551127Z
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 ccc9ae13-7056-44cf-b67c-427209cf66b0 · inbound
CFO: A Framework for Building Production NLP Systems cites this paper.
CFO: A Framework for Building Production NLP Systems End-to-End Open-Domain Question Answering with BERTserini
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:58:19.090162Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d46329f-b95a-4cdf-bbe2-95d5473dddd4 · inbound
A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints cites this paper.
A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints End-to-End Open-Domain Question Answering with BERTserini
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:37:54.094300Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afcd2900-3c70-445f-9067-a42b62e9e2b2 · inbound
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering cites this paper.
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering End-to-End Open-Domain Question Answering with BERTserini
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:50:52.542423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73362987-ce1e-4398-a042-de5fbd55bbf9 · inbound
Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing cites this paper.
Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing End-to-End Open-Domain Question Answering with BERTserini
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:02:19.910334Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e76e8d3a-f596-4325-90de-809355d75a96 · inbound
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models End-to-End Open-Domain Question Answering with BERTserini
Reference 72
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T18:00:50.553208Z
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.
Observation 14e9b474-af90-4672-9f78-bf03265fa12a · inbound
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey End-to-End Open-Domain Question Answering with BERTserini
Reference 169
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T11:32:36.881095Z
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.
Observation a3d7da0b-86ae-4976-b4f6-b369a51bca17 · inbound
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models cites this paper.
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models End-to-End Open-Domain Question Answering with BERTserini
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T17:36:28.856204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.