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Paper Citation Record · LEDGER

Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding

As of 17 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:1908.05161.

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

pith.paper-citation-record.v1
1908.05161 v3

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:26:52.725530Z

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

2 of 2 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c3ab139-3a2a-44d6-9e56-da1b32cb78d7 · outbound

This paper cites DisSent: Sentence Representation Learning from Explicit Discourse Relations.

Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding DisSent: Sentence Representation Learning from Explicit Discourse Relations

Reference 2013

Resolution
unresolved
no resolver link, observed 2026-08-14T13:26:52.725530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:26:52.725530Z digest=sha256:768f4f9091eccfb85bea9c65b0bff399ce5fe97d1cd60ba24aac808eaab33a42

Observation 475cf64d-1b7e-4e30-a835-0f91a9a36860 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding Universal Language Model Fine-tuning for Text Classification

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-14T13:26:52.720193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:26:52.720193Z digest=sha256:47f19ccb15e0d40beab2dd0efdb5502a7eabd2766d2f94403a35af148712ddc3

Pith citing papers

No inbound Pith citation observations are available.