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Paper Citation Record · LEDGER

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1812.10860.

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

pith.paper-citation-record.v1
1812.10860 v5

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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:43:24.445550Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T04:50:03.741716Z

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 018d880c-a2b6-43f8-b2d8-521de4ab6469 · inbound

Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs cites this paper.

Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-08-14T04:50:03.747222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-14T04:50:03.256891Z digest=sha256:e26dcb74885e9c52a884b1af1c0e68cbefcca2aeb59f7cb6934c43ddffaa7c3a

Observation 7a61d678-20dc-478b-8f99-524cff820ccc · inbound

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds cites this paper.

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-16T00:43:24.445550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:43:24.445550Z digest=sha256:6ee94c8f4a4901466a2dcb08f1d3455b5222d4b9b889229f5d57e2a425e53c40