# Identifying Computer-Translated Paragraphs using Coherence Features

arXiv: https://arxiv.org/abs/1812.10896 · HTML: https://mlsanity.com/paper/1812.10896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model](https://mlsanity.com/paper/2511.02958/) (2025)

## Abstract (arXiv)

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.