Multi-modal indicators for estimating perceived cognitive load in post-editing of machine translation

Analysis of Machine Translation and Post-Translation Editing Ability Using Semantic Information Entropy Technology

Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on semantic information entropy are projected, and the semantic Gaussian marginal rectangular window function is obtained. The semantic correlation factors of words are added to the text information entropy and information gain, and the nonlinear spectral properties of adaptive matching semantics are obtained. In this way, it corrects the significant flaw in the way semantic features are extracted using conventional techniques. In order to speed up MT and enhance translation quality, this study proposes automatic post-translation editing to filter those common MT errors that occur frequently and regularly. According to the experimental findings, word translation and segmentation accuracy can both reach 95.27 and 93.12 percent, respectively. In terms of language translation, this approach is accurate and trustworthy. I hope it will serve as a useful source for subsequent research.

Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system.

Translation Accuracy Evaluation of English Complex Long Sentences Based on Multi-Label Clustering Algorithms

In the traditional sense, the translation evaluation of English complex long sentences is often limited to the idea of whether or how to realize the semantic transformation of the original text, so many phenomena that have nothing to do with language but directly affect the translation evaluation are not included in the field of vision and can be interpreted. In order to solve the above problems, a multi-label clustering algorithm is proposed to evaluate the translation accuracy of English complex long sentences. The multi-label clustering algorithm is introduced into the translation evaluation activities to carry out the translation and detection parameters of complex long sentences. The comprehensive description, the accuracy of generalization and the rationality of interpretation lay a solid foundation for English translation activities.

Machine translation of English speech: Comparison of multiple algorithms

In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.

Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process

This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators’ perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned.