BIT ’s system for A uto S imul T rans2021 (2021)

Abstract: In this paper we introduce our Chinese-English simultaneous translation system participating in AutoSimulTrans2021. In simultaneous translation, translation quality and delay are both important. In order to reduce the translation delay, we cut the streaming-input source sentence into segments and translate the segments before the full sentence is received. In order to obtain high-quality translations, we pre-train a translation model with adequate corpus and fine-tune the model with domain adaptation and sentence length adaptation. The experimental results on the evaluation data show that our system performs better than the baseline system.

## introduction machine translation greatly facilitates communication between people of different language, and the current neural machine translation model has achieved great success in machine translation field. however, for some occasions that have higher requirements for translation speed, such as in simultaneous interpretation dynamic subtitles and dynamic subtitles application fields. machine translation models that use full sentences as translation units need to wait for the speaker to speak the full sentence before starting translation, in which the translation delay is unacceptable. in order to reduce the delay, translation must start before the complete sentence is received. but at the same time the incomplete sentence may have grammatical errors and semantic incompleteness, and the translation quality will decrease compared to the result obtained by full sentences. further more, different languages may have different word order. there are also many reordering phenomenon when translating between chinese and english which both belong to the same svo sentence structure. sentence reordering and different word-order expression habits bring a great difficult to simultaneous translation. since the latency of using a full sentence as translation unit is unacceptable, and the translation of incomplete sentences is difficult and not guaranteed to obtain reliable translations, we consider cutting long sentence into appropriate sub-sentences. and each sub-sentence is grammatically correct and semantically complete to get suitable translation result. by decomposing translating long sentences into translating shorter sub-sentences, the translation can be started before the complete long sentence is received. this strategy of achieving low-latency simultaneous translation can be summarized as segmentation strategy @xcite . at the same time, it is observed that a sentence can be divided into independent sub-sentences for translation. for the example in in the following of this paper, the second part will introduce the overall framework of the model, the third part will give a detailed description of the fine-tuning, finally will ex-source sentence 各位 亲爱 的 朋友 们 ， 早上好 ！ target sentence ladies and gentlemen , dear friend s ， good morning . ## system architecture this part mainly introduces the overall framework of our submission in autosimultrans 2021 competition. the whole model uses typical segmentation strategy to achieve simultaneous translation. it consists of a sentence boundary detector and a machine translation module. the sentence boundary detector reads the streaming input text and obtains the appropriate segments. the segments are input to the downstream translation module, and the translation result of each segment is obtained and then spliced to obtain the full translation. the overall framework of the entire model is shown in the figure 1 . ## related work this article uses segmentation strategy to achieve low-latency simultaneous translation. there are also some similar works use segmentation strategy to divide long sentences into segments for translation, @xcite focus on improving the coherence of the subsentences translation results, @xcite focus on solving the problem of longdistance reordering in simultaneous translation. in addition, there are two different strategies for achieving simultaneous translation: one is a more flexible translation strategy based on sentence prefixes. the process of simultaneous translation is defined as a readwrite action sequence from the perspective of behavior. it is necessary to define a suitable strategy to find out the action sequence, and adjust the translator to make the model more suitable for the translation of sentence prefixes @xcite . another type is translation based on dynamic refresh without the need to adjust the machine translation model. whenever the input increases, translate all input and overwrite the translation result that has been generated last time @xcite @xcite . ## conclusion in the end, we obtained translation results that exceeded the baseline model.