---
title: "ERNIE-Doc — paper by Ding et al. (2020) — summary and ideas"
description: "ERNIE-Doc, from Baidu, builds on the recurrence approach and adds three… 2.74 points on TQA. 47 references, cited by 16 papers here."
authors: ["Siyu Ding", "Junyuan Shang", "Shuohuan Wang", "Yu Sun", "Hao Tian", "Hua Wu", "Haifeng Wang"]
year: 2020
venue: "arXiv:2012.15688"
source: "https://arxiv.org/abs/2012.15688"
doi: "10.48550/arxiv.2012.15688"
arxiv: "2012.15688"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ding-2020-ernie-doc-retrospective-long/"
---
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# ERNIE-Doc: A Retrospective Long-Document Modeling Transformer

**Authors:** [Siyu Ding](https://md.research.elamur.ai/free/en/authors/siyu-ding/), [Junyuan Shang](https://md.research.elamur.ai/free/en/authors/junyuan-shang/), [Shuohuan Wang](https://md.research.elamur.ai/free/en/authors/shuohuan-wang/), [Yu Sun](https://md.research.elamur.ai/free/en/authors/yu-sun/), [Hao Tian](https://md.research.elamur.ai/free/en/authors/hao-tian/), [Hua Wu](https://md.research.elamur.ai/free/en/authors/hua-ren-wu/) and [Haifeng Wang](https://md.research.elamur.ai/free/en/authors/haifeng-wang/) · **Year:** 2020 · **Venue:** arXiv:2012.15688 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2012.15688) · [PDF](https://arxiv.org/pdf/2012.15688) · [DOI 10.48550/arxiv.2012.15688](https://doi.org/10.48550/arxiv.2012.15688) · [arXiv 2012.15688](https://arxiv.org/abs/2012.15688) · [OpenAlex](https://openalex.org/W3120952198)

**Citations:** 11 citations (OpenAlex, 2026-10-14) · [16](https://md.research.elamur.ai/free/en/papers/ding-2020-ernie-doc-retrospective-long/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Standard Transformers struggle with long documents because self-attention cost grows quadratically with input length. The usual workarounds each fall short. Chopping text into 512-token pieces cuts links between segments. Sparse attention extends the window only to about 4,096 tokens. Recurrence models such as Transformer-XL see only earlier segments. ERNIE-Doc, from Baidu, builds on the recurrence approach and adds three ideas:

* **Retrospective feed:** the document passes through the model twice, a quick "skim" followed by a second pass in which every segment can draw on the whole text.
* **Enhanced recurrence:** memory is carried within the same layer rather than one layer down, which greatly lengthens the usable context.
* **Segment-reordering objective:** during pretraining, the model learns to recover the original order of shuffled segments.

The English model starts from a RoBERTa checkpoint and is trained on BigBird's corpora. The Chinese model is trained from scratch on ERNIE 2.0 data. ERNIE-Doc sets a new perplexity record on WikiText-103. It also beats RoBERTa, Longformer and BigBird on most classification and QA tasks, offering long-context modeling without custom sparse-attention kernels.

## Key points
- Reaches a perplexity of 16.8 on WikiText-103, a state-of-the-art result for language modeling at publication time.
- Leads base-size models on seven Chinese NLU tasks, including DRCD (dev EM/F1 90.5/95.2), CMRC2018 (76.1/91.6) and C3 (76.5 test accuracy), ahead of ERNIE 2.0 and MacBERT.
- In ablations on ERNIE-Doc-Small, every component contributes:
  - Segment-level recurrence adds 2.74 points on TQA and 3.95 on HYP.
  - Enhanced recurrence adds 2.29 and 1.40.
  - Retrospective feed adds 0.21 and 1.33.
- Segment reordering gives a 1.5-point boost on HYP, a very long-text classification set. This suggests the [CLS] representation benefits from document-level pretraining.
- With enhanced recurrence, a 128-token model roughly matches the accuracy of a 512-token model without it. Code and checkpoints are available in the PaddlePaddle/ERNIE repository.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/) — Company, Beijing, China · 74 works · 15,263 citations

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## Cite

Cite as: Siyu Ding, Junyuan Shang, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu and Haifeng Wang. 2020. ERNIE-Doc: A Retrospective Long-Document Modeling Transformer. arXiv:2012.15688.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ding2020ernie,
  title = {{ERNIE-Doc: A Retrospective Long-Document Modeling Transformer}},
  author = {Siyu Ding, Junyuan Shang, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu and Haifeng Wang},
  year = {2020},
  doi = {10.48550/arxiv.2012.15688},
  eprint = {2012.15688},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2012.15688}
}
```

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*Updated 30 October 2026*