BIT ’s system for the A uto S im T rans 2020 (2020)

Abstract: This paper describes our machine translation systems for the streaming Chinese-to-English translation task of AutoSimTrans 2020. We present a sentence length based method and a sentence boundary detection model based method for the streaming input segmentation. Experimental results of the transcription and the ASR output translation on the development data sets show that the translation system with the detection model based method outperforms the one with the length based method in BLEU score by 1.19 and 0.99 respectively under similar or better latency.

## introduction automatic simultaneous machine translation is a useful technique in many speech translation scenarios. compared with traditional machine translations, simultaneous translation focuses on processing streaming inputs of spoken language and achieving low latency translations. two challenges have to be faced in this task. on one hand, few parallel corpora in spoken language domain are open available, which leads to the fact that the translation performance is not as good as in general domain. on the other hand, traditional machine translation takes a full sentence as input so that the latency of the translation is relatively long. to deal with the shortage of the spoken language corpora, we pre-train a machine translation model on general domain corpus and then fine-tune this model with limited spoken language corpora. we also augment the spoken language corpora with different strategies to increase the in-domain corpora. in order to reduce the translation latency, we use three sentence segmentation methods: a punctuation based method, a length based method and a sentence boundary detection model based method. all of the methods can split the input source sentence into short pieces, which makes the translation model obtain low latency translations. in the streaming automatic speech recognition(asr) output track for the chineseto-english translation task of autosimtrans 2020, most of our proposed systems outperform the baseline systems in bleu score and the sentence boundary detection model based sentence segmentation method abstains higher bleu score than the length based method under similar latency. ## task description we participated in the streaming chineseto-english translation task of autosimtrans 2020 foot_0 : the streaming asr output translation track and the streaming transcription translation track. the two tracks are similar except that the asr output may contain error results and includes no internal punctuation but end punctuation. ## approaches our all systems can be divided into 3 parts: data preprocessing, sentence segmentation and translation. data preprocessing includes data cleaning, data augmentation. we implement 3 sentence segmentation methods, which are based on punctuation, sentence length and a sentence boundary detection model. the training of translation model includes pretraining out of domain and fine-tuning in domain. ## experiments we train our model with the cwmt19 zhen data set, the streaming transcription and the streaming asr output data sets provided by the evaluation organizer. because of the evaluation track limit, we did not use the un parallel corpus and the news commentary corpus although they were used in the baseline. the cwmt19 zh-en data set includes six sub data sets: the casia2015 corpus, the casict2011 corpus, the casict2015 corpus, the datum2015 corpus, the datum2017 corpus and the neu2017 corpus. the cwmt19 data set contains totally 9,023,708 parallel sentences. they are used in the pre-training of our model. streaming transcription and streaming asr output data sets are provided by the evaluation organizer. the transcription data set contains 37,901 pairs and the asr output data set contains 202,237 pairs. we use them as the fine-tuning data to adapt to the spoken language. finally we evaluate our system on the development set which contains 956 pairs. the size of the data set is listed in ## related work end-to-end machine translation models, such as transformer @xcite , greatly promote the progress of machine translation research and have been applied to speech translation researches @xcite @xcite . furthermore, several end-to-end based approaches have recently been proposed for simultaneous translations @xcite . in order to solve the problem of insufficient parallel corpus data for simultaneous translation tasks, @xcite augmented the available training data using backtranslation. @xcite used bert pretraining model to train a large number of external monolingual data to achieve data augmentation. @xcite simulated the input noise of asr model and used placeholders, homophones and high-frequency words to replace the original parallel corpus at the character level. inspired by @xcite , we augment the training data by randomly replacing the words in the source sentences with homophones. in order to reduce the translation latency, @xcite used the prefix-to-prefix architecture, which predicts the target word with the prefix rather than the whole sequence. their wait-k models are used as the baseline and are provided by the shared task organizers. the wait-k models start to predict the target after the first k source words appear. @xcite applied ensemble of models trained with a set of wait-k polices to achieve an adaptive policy. @xcite have proposed a pre-training based segmentation method which is similar to mss. however, in the decoding stage, the time complex of this method is o(n 2 ) whereas the time complex of mss is o(n). ## conclusions in this paper, we describe our submission systems to the the streaming chinese-to-english translation task of autosimtrans 2020. in this system the translation model is trained on the cwmt19 data set with the transformer modedalvi2018incrementall. we leverage homophonic character and word substitutions to augment the fine-tuning speech transcription data set. we implement a punctuation based, a length based and a sentence boundary detection model based sentence segmentation methods to improve the latency of the translation system. experimental results on the development data sets show that the punctuation based sentence segmentation obtains the best bleu score with a reasonable latency on the transcription translation track. the results on the asr outputs translation show the effectiveness of our data augmentation approaches. and the sentence boundary detection model based sentence segmentation gives the low latency and a stable bleu score in our all systems. however, because we have no enough time to retrain the mt model, some settings of our system are not consistent with the baseline, so it is difficult to judge whether our method is better than baseline's method. in the future, we will finish this comparative experiment.