Research Article
An automatic scoring method for Chinese-English spoken translation based on attention LSTM
@ARTICLE{10.4108/eai.13-1-2022.172818, author={Xiaobin Guo}, title={An automatic scoring method for Chinese-English spoken translation based on attention LSTM}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={SIS}, year={2022}, month={1}, keywords={Chinese-English spoken translation, attention LSTM, sentence level}, doi={10.4108/eai.13-1-2022.172818} }- Xiaobin Guo
Year: 2022
An automatic scoring method for Chinese-English spoken translation based on attention LSTM
SIS
EAI
DOI: 10.4108/eai.13-1-2022.172818
Abstract
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173786.
In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spoken fluency as the main parameters of scoring. In order to improve the accuracy of keyword scoring, this paper uses synonym discrimination method to identify the synonyms in the examinees' answer keywords. At the sentence level, attention LSTM model is used to analyze examinees' translation of sentence general idea. Finally, spoken fluency is scored based on tempo/rate and speech distribution. The final translation quality score is obtained by combining the weighted scores of the three parameters. The experimental results show that the proposed method is in good agreement with the result of manual grading, and achieves the expected design goal compared with other methods.
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