BIT -Xiaomi’s System for A uto S im T rans 2022 (2022)

Abstract: This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track and the En-Es test-to-text track. In our system, wait-k is employed to train prefix-to-prefix translation models. We integrate streaming chunking to detect boundaries as the source streaming read in. We further improve our system with data selection, data-augmentation and R-drop training methods. Results show that our wait-k implementation outperforms organizer’s baseline by 8 BLEU score at most, and our proposed streaming chunking method further improves about 2 BLEU in low latency regime.

## introduction simultaneous translation @xcite @xcite , is a task in machine translation (mt), which intends to provide low latency translation in real-time scenarios. to achieve low latency translation, the translation system needs to begin translating before the end of source sentences, which can be viewed as prefix-toprefix translation @xcite . simultaneous translation is widely used in real-time translation scenarios such as simultaneous interpretation, online subtitles, and live broadcasting. in these scenarios, low latency may have equal or even higher priority than translation quality. in simultaneous translation, the most challenge is the balance of translation quality and latency. low latency translation requires beginning translation with insufficient source information, which may cause incorrect translation results. how to find a simultaneous policy to balance quality and latency is the most challenging question. on another hand, in most cases, the standard machine translation model is trained on full sentences, which can achieve good performance in full-sentence evaluation. but for prefix-to-prefix inference, which is crucial for simultaneous translation, the standard machine translation model always perform poorly. previous methods for simultaneous translation can be classified as the fixed policy and the adaptive policy according to different simultaneous policies. fixed policy uses fixedlatency simultaneous strategy, for example, set value k, and forces the translation to lag behind source for k tokens @xcite . the adaptive policy needs an agent module to perform adaptive simultaneous translation. the agent will consider the current translation state, including the source prefix and the hypothesis prefix, to decide whether to output new tokens at the current state @xcite @xcite . chunk-base @xcite simultaneous translation is a special adaptive policy, which makes a decision only based on the source prefix. in our system, we propose a streaming chunking method that can be combined with a fixed wait-k policy. the streaming chunking method can significantly improve translation quality with little latency increase in low latency regions. we train a segmentation model to detect boundaries in streaming sources and employ a wait-k policy to decide output token numbers. we pre-train transformer models with multi-path wait-k on a large general corpus and fine-tune with single k on a small domain corpus. we augment the general corpus and domain corpus with back-translation (bt) and front-translation (ft), and further augment the domain corpus with character-level pseudo asr error. in training we incorporate r-drop (liang et al., 2021) method to improve translation quality. in text-to-text tracks, we use text streaming input provided by the organizer. in the audio-totext track, we train our asr system to transcript audio into the streaming text as translation input. the remainder of this paper is organized as follows. we describe the techniques employed in our system and the methods we propose in section 2. in section 3 we show our experiment settings and results, including data and model. finally, we conclude this paper. ## methods in this section, we describe the data, the utilized prefix-to-prefix translation model, and the proposed streaming chunking method. ## conclusion we elaborate on the bit-xiaomi simultaneous translation system in this paper. we investigate data filtering and augmentation to enlarge high-quality corpus and utilize the r-drop method to improve translation quality. we train our simultaneous translation models based on the wait-k strategy, and the streaming chunking method is employed to avoid segmentation errors in the source stream. the results on zh-en text-to-text track indicate that the streaming chunking method can be integrated with the streaming decoding and improves translation quality. the slightly worse quality on the audio track suggests that the asr error may affect translation quality much. in the future, we will explore better streaming asr models and try more interesting simultaneous policies to get better latency and quality.