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Paper Citation Record · LEDGER

Identifying Well-formed Natural Language Questions

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1808.09419.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1808.09419 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:46:42.203387Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T12:18:45.195808Z

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 9579f8e8-d06c-4278-ab52-e33f84c4d1cc · inbound

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System cites this paper.

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System Identifying Well-formed Natural Language Questions

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-14T13:46:42.203387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:46:42.203387Z digest=sha256:c1c63a4d9a49c38f87eb210d2fd150777a1fbe42fb906ab98d899ca6decaa276

Observation 2bb94d1c-e018-4e09-99e0-dd156d51b95c · inbound

Bridging the Data Provenance Gap Across Text, Speech and Video cites this paper.

Bridging the Data Provenance Gap Across Text, Speech and Video Identifying Well-formed Natural Language Questions

Reference 250

Resolution
verified exact
local_arxiv, observed 2026-08-11T12:18:45.201314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T12:18:42.956295Z digest=sha256:6b9f1c306186a5214244d1cc24171b429b5de9d96b353d9a3c90c0aea3ca768d