Machine Translation Quality Estimation Algorithm Based on Intelligent Fuzzy Decision Tree Algorithm
DOI: 10.23977/acss.2022.060709 | Downloads: 14 | Views: 408
Author(s)
Ruichao Li 1
Affiliation(s)
1 School of Translation Studies, Xi'an Fanyi University, Xi'an, Shaanxi, China
Corresponding Author
Ruichao LiABSTRACT
With the development and application of machine translation(MT) technology, MT results appear in more scenarios, but the translation quality cannot be guaranteed, and users need to know the quality of MT results to decide whether to adopt them or not. MT quality estimation (QE) is a key task in the field of MT, which can score the quality of a translation based only on the source language sentences and the MT. Unlike the methods of automatic translation evaluation, translation QE does not require the use of reference translation, which can save a lot of manpower and resources and is suitable for large-scale MT quality assessment scenarios without reference translation. In this paper, a phrase-structured syntactic tree is constructed based on an intelligent fuzzy decision tree(DT) algorithm, and the sentence-level and word-level QE results of MT translation are analyzed based on this tree structure. By comparing the training prediction results of translation QE models, it is obtained that the method of fusing dependent words with source language(SL) sequences is more helpful to the translation QE process than the method of fusing phrase structure features with source language sequences.
KEYWORDS
Machine Translation, Intelligent Fuzzy Decision Tree Algorithm, Translation Quality Estimation, Phrase Structure Syntax TreeCITE THIS PAPER
Ruichao Li, Machine Translation Quality Estimation Algorithm Based on Intelligent Fuzzy Decision Tree Algorithm. Advances in Computer, Signals and Systems (2022) Vol. 6: 60-66. DOI: http://dx.doi.org/10.23977/acss.2022.060709.
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