---
title: "Authorship Attribution for Neural Text — Uchendu 2020"
description: "This EMNLP 2020 study treats the detection of neural text generators as an authorship problem."
authors: ["Adaku Uchendu", "Thai Le", "Kai Shu", "Dongwon Lee"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.673/"
doi: "10.18653/v1/2020.emnlp-main.673"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/uchendu-2020-authorship-attribution-neural-text-generation/"
---
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# Authorship Attribution for Neural Text Generation

**Authors:** Adaku Uchendu, Thai Le, Kai Shu, [Dongwon Lee](https://md.research.elamur.ai/free/en/authors/dongwon-lee/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.673/) · [PDF](https://aclanthology.org/2020.emnlp-main.673.pdf) · [DOI 10.18653/v1/2020.emnlp-main.673](https://doi.org/10.18653/v1/2020.emnlp-main.673) · [OpenAlex](https://openalex.org/W3101891351)

**Citations:** 118 citations (OpenAlex, 2026-10-14) · [77](https://md.research.elamur.ai/free/en/papers/uchendu-2020-authorship-attribution-neural-text-generation/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
This EMNLP 2020 study treats the detection of neural text generators as an authorship problem. It asks three questions. First, do two texts come from the same source? Second, was a text produced by a person or by a machine? Third, which of k = 8 candidate generators produced a given text? The authors assembled a political-news corpus with nine sources. The human part is professionally written articles. The other eight parts were produced by CTRL, GPT, GPT2, GROVER, XLM, XLNET, PPLM and FAIR, each prompted with the human headlines and asked for 500 words. The first two questions were framed as binary classification and the third as multi-class classification. The authors tested classical learners on stylistic features, several CNN/RNN architectures, n-gram and POS-based attribution methods, and pretrained detectors (RoBERTa, GROVER-DETECT). Most generators turned out to be easy to separate from human writing. GPT2, GROVER and FAIR produced more convincing text and confused the classifiers most. The work matters for tracing disinformation back to a specific generator, and the code and data are publicly released.

## Key points
- The human data consists mostly of political news: 819 CNN, 132 Washington Post and 113 New York Times articles. Their headlines served as prompts for all eight generators.
- Same-source detection (P1) is easier when the classes are balanced, reaching F1 = 0.9813. The imbalanced setting is noticeably harder.
- For human vs. machine detection (P2), fine-tuned RoBERTa performed best overall and reached F1 = 0.9792 on GPT2 text. GROVER-DETECT did well only on GROVER output (F1 = 0.8135).
- For attributing a text to a specific generator (P3), Random Forest on linguistic features achieved the highest average F1 (0.9042). CTRL, XLM and XLNET were easy to identify, while Human, GROVER, FAIR and GPT2 were the hardest.
- The most useful stylistic cues were word count, articles, periods, words per sentence, auxiliary verbs, prepositions and commas. A topic-based baseline (LDA topics with bigrams and trigrams) scored only 0.38 F1. This suggests topic differences alone do not explain the attribution results.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/)

## Cite

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

```bibtex
@inproceedings{uchendu2020authorship,
  title = {{Authorship Attribution for Neural Text Generation}},
  author = {Uchendu, Adaku and Le, Thai and Shu, Kai and Lee, Dongwon},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.673},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.673}
}
```

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54. Balanced P1 P2 P3 \\\#train 68,896 2,985 6,825 \\\#valid 7,656 426 881 \\\#test 19,139 853 1,888 Imbalanced P1 P2 \\\#train 62,157 6,825 \\\#valid 6,907 881 \\\#test 17,266 1,888 *Table 8: Details on Train, Validation and Test Set Splits* [find](https://scholar.google.com/scholar?q=Balanced+P1+P2+P3+%5C%5C%5C%23train+68%2C896+2%2C985+6%2C825+%5C%5C%5C%23valid+7%2C656+426+881+%5C%5C%5C%23test+19%2C139+853+1%2C888+Imbalanced+P1+P2+%5C%5C%5C%23train+62%2C157+6%2C825+%5C%5C%5C%23valid+6%2C907+881+%5C%5C%5C%23test+17%2C266+1%2C888+%2ATable+8%3A+Details+on+)