OPPO NMT System for IWSLT 2019 (2019)

Abstract: This paper illustrates the OPPO's submission for IWSLT2019 text translation task Our system is based on Transformer architecture. Besides, we also study the effect of model ensembling. On the devsets of IWSLT 2019, the BLEU of our system reaches 19.94.

## introduction neural machine translation has recently received huge attention and has been introduced as a promising paradigm with the potential to overcome any shortcomings of traditional statistical machine translation system. @xcite in this paper, we describe the oppo 's submission about the neural machine translation system for the iwslt 2019 english to czech translation task. the neural machine translation system consists of an encoder and a decoder, while the encoder embeds tokens of the source sentence into a series of feature vectors which are used by the decoder to generate translation results. besides, attention is also employed to better model the link between the source sentences and the target sentences. ## model description neural machine translation exploits an encoder-decoder framework to model the whole translation process in an end-to-end fashion, and has achieved state-of-the-art performance in many language pairs. among various translation models, the transformer model based on self-attention mechanism has shown promising results in terms of both translation performance and training speed. the model architecture is shown in figure 1 . we also us the transformer architecture. we also use bpe technology to alleviate the unk problem. besides, model ensembling is used to further improve the final results of the system. we utilize layer normalization @xcite to adaptively learn to scale and shift the incoming activations of a neuron on a layer-by-layer basis at each time step. layer normalization can stabilize the dynamics of layers in the network and accelerate the convergence speed of deep neural networks. the two components of the transformer are the encoding and decoding framework, which are composed of and linked by attention mechanism, while the position encoding provide the order information of the sentences.