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Paper Citation Record · LEDGER

SpanBERT: Improving Pre-training by Representing and Predicting Spans

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:1907.10529.

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

pith.paper-citation-record.v1
1907.10529 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:42:20.343418Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:06:20.155007Z

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 a0ca66fe-ea1f-4119-a519-3abbb8b02c74 · inbound

RoBERTa: A Robustly Optimized BERT Pretraining Approach cites this paper.

RoBERTa: A Robustly Optimized BERT Pretraining Approach SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T04:47:44.471482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-09T04:47:43.784327Z digest=sha256:100b7fcb05c07c26a76bfc3e51e804742f16a7082afda0d97a96b30ad62acbe9

Observation 9a0c267a-909b-4654-99e0-ada48203b61e · inbound

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding cites this paper.

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-14T13:42:20.343418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:42:20.343418Z digest=sha256:8438c4f3791a70eb304214e4e9736aa6086f81455a6bdb864b08623a1986736e

Observation e016dfbd-a9c0-48ca-baca-46e94866a118 · inbound

BERT for Coreference Resolution: Baselines and Analysis cites this paper.

BERT for Coreference Resolution: Baselines and Analysis SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-14T11:25:13.383015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T11:25:13.383015Z digest=sha256:b22cce5fea50226177bf68e18869d70a1246171ec6fee8802e517b2fcae5b112

Observation bd8e9558-8909-41a5-a3d1-ad5ffabde605 · inbound

Effective Use of Transformer Networks for Entity Tracking cites this paper.

Effective Use of Transformer Networks for Entity Tracking SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-14T04:49:53.592371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:49:53.592371Z digest=sha256:7b7683d3509675dee408d5bfcbd225499fc0610ec51fa1263a507da87ee17b6d

Observation cbe37ed2-4c6e-457f-b06f-8b114db5ebc3 · inbound

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism cites this paper.

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T18:34:44.844288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T18:34:44.807534Z digest=sha256:648286f6ff9ee89694a4b947912d465f5548d92b665764fc49eeafce56427b46

Observation 7b8c01b3-3773-4ba2-a8b8-bda919905e0f · inbound

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations cites this paper.

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-13T12:26:58.106271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-13T12:26:58.015594Z digest=sha256:217c34b3e4f6d65814d6bf9ea64e5b9511441b9fa6029a552f470ae4913eccad

Observation ae94d6f6-95a6-4c65-9338-b09d49e1de76 · inbound

HuggingFace's Transformers: State-of-the-art Natural Language Processing cites this paper.

HuggingFace's Transformers: State-of-the-art Natural Language Processing SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 158

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:53:59.704523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-11T14:53:58.963468Z digest=sha256:e8b7a32b95afc8ba6bb6300ad9bd3b795cf569d300fb0f34547795328388c8d3

Observation 430b9fb1-7549-44a2-8769-f8771c58fc4f · inbound

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer cites this paper.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:37:55.670820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-12T05:37:55.083206Z digest=sha256:2fe7482cfea991aa2030cc8330c948e748f692c06f204dcd70114998d915af44

Observation 42d0e3b1-869a-4c90-8ca2-a3040099665b · inbound

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension cites this paper.

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-13T00:14:58.192732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-13T00:14:58.134513Z digest=sha256:7641cf4c3099e6e070f8bfd8c241390011e81233450cc2b7c061cd268843df45

Observation 1d583409-88ae-4b2d-bcac-c99bbb2b0d65 · inbound

REALM: Retrieval-Augmented Language Model Pre-Training cites this paper.

REALM: Retrieval-Augmented Language Model Pre-Training SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T09:59:16.190100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T09:59:16.120886Z digest=sha256:81f5e2c5ef67a7db7a4d3421d06e9056f7fba27156f6cc8e103cbbbd3fee59f1

Observation f7341c57-118d-43e9-8a38-7c62b45c2aaa · inbound

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators cites this paper.

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-16T10:26:47.622756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-16T10:26:47.593122Z digest=sha256:333fff87254c0d9d280fab3969a01eac51e8ff4824c12d4e7206304bd00b6d75

Observation a5347c99-5f8e-4aa2-8ce8-f948386f269c · inbound

Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications cites this paper.

Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T04:31:52.216952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T04:31:52.216952Z digest=sha256:9c1eec25569941317f72f2d991c1ce264298c509d32a448050efdc942810dfa1

Observation 944e1e30-be0f-42b7-86e2-d928a92ab48d · inbound

Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats cites this paper.

Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T14:22:37.372310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:22:37.372310Z digest=sha256:d1095b744ef2afe8a21feaf5cf51f7de55684538f2eeb9753e56bf690feae00f

Observation 0bebc808-59c2-4280-8177-603321b17fa1 · inbound

Structure-Aware Fill-in-the-Middle Pretraining for Code cites this paper.

Structure-Aware Fill-in-the-Middle Pretraining for Code SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T12:14:22.318226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:14:22.318226Z digest=sha256:4573cae6b1d189e5af5d978fd7b757378ace7aa14e6f4057264c240b245b1b06

Observation 85d5bbcc-9580-481b-8dde-c40165ad7543 · inbound

Masked Diffusion Language Models with Frequency-Informed Training cites this paper.

Masked Diffusion Language Models with Frequency-Informed Training SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T05:46:28.123379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:46:28.123379Z digest=sha256:079852edd3afd4ae9228c3feba1b6072ed4e6549af6e5c191c08fa733750f334

Observation 2c4c862b-c822-4273-909d-e0d0097894d6 · inbound

PortBERT: Navigating the Depths of Portuguese Language Models cites this paper.

PortBERT: Navigating the Depths of Portuguese Language Models SpanBERT: Improving Pre-training by Representing and Predicting Spans

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-01T23:06:20.156895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-28T14:43:28.401687Z digest=sha256:a7f2bff9f50468b5e4762ea47beaa859cc5c0e7d8870977f99e3c1d13a13d1a5