基于机器学习的文学作品英译自动评价

为了提高文学英译作品自动评价的水平,引入基于机器学习的智能算法模型成为当前最有效的方法.首先研究文学作品的翻译规则和特殊性,建立基于变量特征的翻译评价指标体系;然后利用Python语言平台,英译文本经Stanford Parser、NLTK等工具包过滤预处理之后,采取VSM向量空间模型获得特征编码和特征度,再输入到Random-RF、Original-RF和AHP-RF算法模型中训练学习,完成翻译质量评价与分析.实验结果表明,融合层次分析法、灰色关联法和随机森林算法的AHP-RF模型的分类效果优于其它2种,同时人工译本相较于其它4种机器译本,质量评分高、分类错误率小,评价结果与实际翻译情况吻合.

Automatic Evaluation for English Translation of Literary Works Based on Machine Learning

The intelligent algorithm model based on machine learning has become the most effective method at present to improve the automatic evaluation for the English translation of literary works. First, the translation rules and particularity of literary works are studied, and the index system of translation evaluation based on the variable features is established. Then, with the aid of the Python language platform, after the English translation is filtered and preprocessed by tools such as Stanford Parser and NLTK, the feature codes and feature degree are obtained with the Vector Space Model (VSM). Furthermore, the results are input into the Random-RF, Original-RF, and AHP-RF algorithm models for training and learning. Thus, the evaluation and analysis of translation quality are completed. The experimental results show that the AHP-RF model combining the analytic hierarchy process, the grey correlation method, and the random forest algorithm has better classification than the other two. Meanwhile, compared with the other four machine translation versions, the manual translation has a high quality score and a low classification error, and the corresponding evaluation results are consistent with the actual translation.