System Description on Automatic Simultaneous Translation Workshop (2021)

Abstract: This paper shows our submission on the second automatic simultaneous translation workshop at NAACL2021. We participate in all the two directions of Chinese-to-English translation, Chinese audio \rightarrow English text and Chinese text \rightarrow English text. We do data filtering and model training techniques to get the best BLEU score and reduce the average lagging. We propose a two-stage simultaneous translation pipeline system which is composed of Quartznet and BPE-based transformer. We propose a competitive simultaneous translation system and achieves a BLEU score of 24.39 in the audio input track.

## introduction our submitted system consists of an end to end speech recognition model and a neural machine translation model which follows the traditional pipeline framework in simultaneous translation task. the system input is chinese audio file and the output is english translation text. a temporary streaming transcription is obtained by speech recognition model and transmitted into machine translation model to get the target system output. for automatic speech recognition(asr) model, we use the quartznet model @xcite of nvidia jarvis. at the moment, we expand the train data set by adding aishell-1 and data that collected, then using plenty of rules to filter audio data and deal with parallel transcription. compared to the jasper model @xcite , it can reduce number of parameters quickly by using separable 1d convolutions including time channel. our neural machine translation model is transformer @xcite . we use some human rules and the pre-trained language model to filter the parallel corpus. the method of back translation @xcite is also applied to generate synthetic chinese sentences. at the step of inference, we apply the wait-k words method @xcite . both the preprocessing and post-processing are applied to improve the terminology translation and deal with the word error produced by the asr system. since our submission is a two-stage system, the rest of this paper describes separately regards to the automatic speech recognition(asr and machine translation(mt) sub-modules. we firstly describe the training and development datasets we used, then the data filtering methods we applied is introduced. secondly, the system architecture is discussed and it is verified by the experiments. lastly, we draw a conclusion of our system by analyzing the experiments. ## datasets for audio data of asr, we use qianyan audio datasets provided by naacl workshop @xcite , aishell-1 (hui bu, 2017) and lip sentences we collect by smartphone(16khz, 16-bit). ## system description the model training process for both the speech recognition and machine translation model are implemented on a device with eight gpus of nvidia tesla v100. ## conclusion this paper describes our submission to the second automatic simultaneous translation workshop at naacl2021. we detail our process of data filtering and model training. the consecutive wait(cw) @xcite of the best point reached to 18.4 while we get the bleu value of 24.39 in the audio input track. in future work, we will continue to research on end-to-end speech translation model from chinese speech input to english text output.