Paper Citation Record · LEDGER

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

As of 22 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:1909.00204.

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

pith.paper-citation-record.v1
1909.00204 v3

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T06:03:48.200712Z

measured 19 of 19 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:51:59.392573Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T22:52:00.123793Z

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy6
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd21789d-5326-415a-851c-d6d4e63dad04 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.479744Z

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.

Observation 4abb8db3-aad3-4a43-9d2f-7b5f89c54678 · outbound

This paper cites Deep contextualized word representations.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Deep contextualized word representations

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.462993Z

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.

Observation f521cad7-1ef3-423d-a86c-1d5300db7b5a · outbound

This paper cites ERNIE: Enhanced Representation through Knowledge Integration.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding ERNIE: Enhanced Representation through Knowledge Integration

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.129361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52c3f7f0-ec04-4733-a686-1614f376c8fd · outbound

This paper cites ERNIE 2.0: A Continual Pre-training Framework for Language Understanding.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.134646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1e772a-ca6f-4859-9f34-8841411c2429 · outbound

This paper cites ERNIE: Enhanced Language Representation with Informative Entities.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding ERNIE: Enhanced Language Representation with Informative Entities

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.139928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5212cd9a-553f-4f30-8472-a268629bcca5 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.145052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43eb274d-c42b-4867-b40b-5ad3897fcc8e · outbound

This paper cites Roberta: A robustly optimized bert pretraining approach.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Roberta: A robustly optimized bert pretraining approach

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.448298Z

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.

Observation 6761bf4b-ecd2-40d5-a7a1-99201cb76568 · outbound

This paper cites Pre-Training with Whole Word Masking for Chinese BERT.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Pre-Training with Whole Word Masking for Chinese BERT

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.154856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd584831-222f-4d1a-97ae-5b1259e04ceb · outbound

This paper cites Attention is all you need.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Attention is all you need

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.435108Z

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.

Observation 7af71fc9-03c8-47fd-981f-2a343048c32b · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.163017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea250b8d-ea48-43c3-ad30-e1b26753a102 · outbound

This paper cites Self- attention with relative position representations.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Self- attention with relative position representations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.421248Z

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.

Observation 996f7f16-ccf8-4f6a-9a99-34fa66f70bf3 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.171007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6322dc2b-bd78-45cb-a1af-28900c54d814 · outbound

This paper cites Mixed Precision Training.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Mixed Precision Training

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.175704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 505b88f1-c4bf-492c-9d22-e82218e9a916 · outbound

This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.180629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eac387e-7caf-4521-8394-22f90af152ed · outbound

This paper cites Horovod: fast and easy distributed deep learning in TensorFlow.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Horovod: fast and easy distributed deep learning in TensorFlow

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.185405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c8cf48-064a-4cd2-8fe9-7bdd8f4ffe2e · outbound

This paper cites A Span-Extraction Dataset for Chinese Machine Reading Comprehension.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.190523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc5e7177-6e5b-49ae-8150-9e34889cfeea · outbound

This paper cites XNLI: Evaluating Cross-lingual Sentence Representations.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding XNLI: Evaluating Cross-lingual Sentence Representations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T06:03:48.195294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a86bd0c4-a207-412e-95bf-90fafe1ea537 · outbound

This paper cites Lcqmc: A large- scale chinese question matching corpus.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding Lcqmc: A large- scale chinese question matching corpus

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T06:03:48.405629Z

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.

Pith citing papers

Observation 3be51007-a3ad-4cd9-b48b-4f429564ac7e · inbound

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction cites this paper.

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction NEZHA: Neural Contextualized Representation for Chinese Language Understanding

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:52:00.130216Z

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.