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Paper Citation Record · LEDGER

LSTM-based Deep Learning Models for Non-factoid Answer Selection

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1511.04108.

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

pith.paper-citation-record.v1
1511.04108 v4

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 2026-08-14T13:46:42.288472Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T12:37:54.219260Z

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 37ee7258-46a2-4abd-ae04-c9815e669691 · 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 LSTM-based Deep Learning Models for Non-factoid Answer Selection

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-14T13:46:42.288472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:46:42.288472Z digest=sha256:ea44734255dca3c09d78de00107b94df05fbee5d09b1834700a673d1a7b6c970

Observation 418bb51b-a64c-401a-b6a6-089be5d56529 · 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 LSTM-based Deep Learning Models for Non-factoid Answer Selection

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-14T12:37:54.230175Z

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=arxiv_source observed=2026-08-14T12:37:54.076489Z digest=sha256:d20f8ee84f8407d221da3d19bc86205a5d2a0e93c59466162a72410513732367