Identifying Computer-Translated Paragraphs using Coherence Features
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
Gender Bias in English-to-Greek Machine Translation
Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
How Programming Concepts and Neurons Are Shared in Code Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.