Hybrid System Combination Framework for Uyghur–Chinese Machine Translation

Abstract

Both the statistical machine translation (SMT) model and neural machine translation (NMT) model are the representative models in Uyghur–Chinese machine translation tasks with their own merits. Thus, it will be a promising direction to combine the advantages of them to further improve the translation performance. In this paper, we present a hybrid framework of developing a system combination for a Uyghur–Chinese machine translation task that works in three layers to achieve better translation results. In the first layer, we construct various machine translation systems including SMT and NMT. In the second layer, the outputs of multiple systems are combined to leverage the advantage of SMT and NMT models by using a multi-source-based system combination approach and the voting-based system combination approaches. Moreover, instead of selecting an individual system’s combined outputs as the final results, we transmit the outputs of the first layer and the second layer into the final layer to make a better prediction. Experiment results on the Uyghur–Chinese translation task show that the proposed framework can significantly outperform the baseline systems in terms of both the accuracy and fluency, which achieves a better performance by 1.75 BLEU points compared with the best individual system and by 0.66 BLEU points compared with the conventional system combination methods, respectively.

A 2D Convolutional Gating Mechanism for Mandarin Streaming Speech Recognition

“…Therefore, several models based on attention mechanisms have been proposed to make it possible for Transducer models to exploit contextual information. Transformer-Transducer (T-T) [15,16] has been proposed on speech recognition, with Transformer [17] becoming the state-of-the-art approach in the language modeling and machine translations fields [18][19][20]. They replaced LSTM with the encoder part of Transformer, which mainly includes multi-head attention mechanisms, feedforward networks, and layer normalization, have been proposed on speech recognition.…”