Evaluation of Machine Translation Based on Semantic Dependencies and Keywords

TL;DR

This paper introduces a semantic-based evaluation method for machine translation that incorporates semantic dependencies and keywords, leading to more accurate assessments of semantic correctness compared to existing approaches.

Contribution

It proposes a novel evaluation algorithm that combines semantic dependency analysis and keyword information to improve the accuracy of semantic correctness measurement in machine translation.

Findings

Enhanced evaluation accuracy over similar methods

More precise measurement of semantic correctness

Effective integration of semantic dependencies and keywords

Abstract

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.