XMU ’s Simultaneous Translation System at NAACL 2021 (2021)

Abstract: This paper describes our two systems submitted to the simultaneous translation evaluation at the 2nd automatic simultaneous translation workshop.

## introduction simultaneous translation refers to translating the message from the speaker to the audience in realtime without interrupting the speaker. it is widely used in many scenarios such as international conferences and business negotiations. simultaneous machine translation is a challenging task and has become an increasingly popular research field in recent years. there have been some researches on simultaneous translation of speech input @xcite @xcite , and some researches focused on text translation @xcite @xcite . in this paper, we describe our two systems submitted to the simultaneous translation evaluation at the 2nd automatic simultaneous translation workshop, which are for zh->en text-to-text translation and zh->en speech-to-text translation. we build our systems with the state-of-the-art method @xcite , and verify the effectiveness of this method. ## text-to-text track in this section, we describe our system submitted to zh->en text-to-text simultaneous translation track. the main idea of this system is how human interpreters work. while listening to speakers, human interpreters constantly translate text segments that are appropriate to translate without waiting for more words, and meanwhile making the translation grammatically tolerable. text segments considered appropriate to translate usually have clear and definite meaning, because the translation of such a segment is not likely to be changed by subsequent text. the authors of @xcite refered to such segments meaningful units (mu) and gived mus a precise difinition. see our system works like a human interpreter described above and is composed of an mu classification model and a machine translation model. once a segment is classified to be an mu by the mu classifier, the mt model uses forced decoding with previous translation as the prefix to translate the segment, as shown in ## speech-to-text track in this section, we describe our system submitted to zh->en speech-to-text simultaneous translation track. this system is a pipeline of three stages: (1) speech recognition, (2) punctuation restoration, and (3) streaming machine translation. the third stage is exactly the same as the system described in section 2. in other words, the system described in section 2 is a part of our speech-to-text system, and therefore it will not be repeated in this section. this section only describes the stage (1) and stage (2). ## experiments in this section, we evaluate our two systems on the development set of the baidu speech translation corpus @xcite . the two used metrics are case-sensitive detokenized bleu @xcite and consecutive wait (cw) @xcite , for translation quality and latency respectively. cw considers on how many source words are waited for consecutively between two target words, and thus larger cw means longer latency. we use sacrebleu @xcite to compute bleu scores. ## conclusion we have built two systems for text-to-text simultaneous translation and speech-to-text simultaneous translation. the key of our systems is the basic adaptive segmentation policy in @xcite . with this policy, simultaneous translation can be achieved without any modification to the mt component, and the latency can be controlled. in our future work, we would like to study how to improve the cooperation of asr and mt and the performance of mu classification.