In view of the fact that most of the existing machine translation evaluation
algorithms only consider the lexical and syntactic information, but ignore the
deep semantic information contained in the sentence, this paper proposes a
computational method for evaluating the semantic correctness of machine
translations based on reference translations and incorporating semantic
dependencies and sentence keyword information. Use the language technology
platform developed by the Social Computing and Information Retrieval Research
Center of Harbin Institute of Technology to conduct semantic dependency
analysis and keyword analysis on sentences, and obtain semantic dependency
graphs, keywords, and weight information corresponding to keywords. It includes
all word information with semantic dependencies in the sentence and keyword
information that affects semantic information. Construct semantic association
pairs including word and dependency multi-features. The key semantics of the
sentence cannot be highlighted in the semantic information extracted through
semantic dependence, resulting in vague semantics analysis. Therefore, the
sentence keyword information is also included in the scope of machine
translation semantic evaluation. To achieve a comprehensive and in-depth
evaluation of the semantic correctness of sentences, the experimental results
show that the accuracy of the evaluation algorithm has been improved compared
with similar methods, and it can more accurately measure the semantic
correctness of machine translation.

通过语言技术平台，本文提出了一种计算方法，用于评估基于参考翻译并结合语义依赖和句子关键词信息的机器翻译的语义正确性，并通过实验证明与类似方法相比，该评估算法的准确性得到了提高，能更准确地测量机器翻译的语义正确性。