Method and apparatus for assessing translation quality

Abstract

Embodiments of the present disclosure disclose methods and apparatus for evaluating translation quality. A specific implementation of the method includes: obtaining the translation to be evaluated and the reference translation; inputting the translation to be evaluated and the reference translation into a pre-trained paraphrase coding model to obtain the semantic similarity between the translation to be evaluated and the reference translation, wherein the paraphrase coding model is A neural network used to calculate the probability that a pair of sentences are paraphrased sentences; the translation to be evaluated and the reference translation are analyzed into two syntax trees through a syntax analyzer; the similarity of the two syntax trees is calculated as the translation to be evaluated and the reference translation Text similarity; the weighted sum of semantic similarity and text similarity is used as the translation quality score. In this embodiment, by combining the scores of several indicators, it can reflect the comprehensive evaluation of the current translation in terms of semantics, syntax, morphology, and overall information intelligibility, avoiding the situation where the automatic evaluation score is high but the intelligibility is low.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method and device for evaluating translation quality. Background technique [0002] Traditional automatic translation quality evaluation methods generally use BLEU (bilingual evaluation understudy, bilingual mutual translation quality auxiliary tool), METEOR (An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments, an automatic machine translation with improved correlation with human judgments). Evaluation method) and other indicators to evaluate the translation, focusing on the degree of matching between the original text and the target reference translation. For example, BLEU is more concerned with the matching degree of continuous strings, that is, n-grams. Some indicators such as METEOR are supplemented on the basis of BLEU, which can not only match strings with the same text, but also identify strings with diffe...

AI Technical Summary

Problems solved by technology

In this case, adopting the traditional automatic evaluation method may easily lead to low scores and inaccurate evaluation results due to too much missing translation information.

For example, using traditional methods to evaluate, the translation rate of an interpreter with many years of experience is only about 70%, and the translation acceptability is lower than 85%.

[0004] Traditional translation quality assessment methods have high labor costs, and at the same time, manual assessment is limited by the translation level of different human evaluators, resulting in large fluctuations in assessment results

It is necessary to construct a reference translation separately for the interpretation scene, and at the same time, the traditional method cannot distinguish the important information in the translation, and the quality of the translation is measured completely according to the fidelity

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