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Multilingual neural machine translation (MNMT) with a single model has drawn more attention due to its capability to deal with multiple languages.However,the current multilingual translation paradigm does not make use of the similar features embodied in different languages,which has already been proven useful for improving the multilingual translation.Besides,the training of multilingual model is usually very time-consuming due to the huge amount of training data.To address these problems,we propose a similarity-based curriculum learning method to improve the overall performance and convergence speed.We propose two hierarchical criteria for measuring the similarity,one is for ranking different languages (inter-language) with singular vector canonical correlation analysis,and the other is for ranking different sentences in a particular language (intra-language) with cosine similarity.At the same time,the paper proposes a curriculum learning strategy that takes the loss of validation set as the curriculum replacement standard.We conduct experiments on balanced and unbalanced IWSLT multilingual data sets and Europarl corpus datasets.The results demonstrate that the proposed method outperforms strong multilingual translation systems and can achieve up to a 64% decrease in training time.

Phones for 239 non-annotated languages were selected by automatic segmentation based on changes of energy in the time-frequency representation of speech signals. Phone boundaries were set at location of relatively major changes in energy distribution between seven frequency bands. A vector of average energies calculated for eleven frequency bands was chosen as the representation of a single phone. We focus our research on an unsupervised comparison of phone distribution in 239 languages. Using the hierarchical clustering method, the relationship between the number of clusters and Ward's distance was determined. A mathematical model is proposed to describe this dependency. Its four parameters are determined for each language individually to model the relationship between the number of clusters and the frequency diversity of phones contained in clusters. We used these relationships to compare languages and to create their ranking based on the size of phone varieties in the frequency domain. [ABSTRACT FROM AUTHOR]