ICT ’s System for A uto S im T rans 2021: Robust Char-Level Simultaneous Translation (2021)

Abstract: Simultaneous translation (ST) outputs the translation simultaneously while reading the input sentence, which is an important component of simultaneous interpretation. In this paper, we describe our submitted ST system, which won the first place in the streaming transcription input track of the Chinese-English translation task of AutoSimTrans 2021. Aiming at the robustness of ST, we first propose char-level simultaneous translation and applied wait-k policy on it. Meanwhile, we apply two data processing methods and combine two training methods for domain adaptation. Our method enhance the ST model with stronger robustness and domain adaptability. Experiments on streaming transcription show that our method outperforms the baseline at all latency, especially at low latency, the proposed method improves about 6 BLEU. Besides, ablation studies we conduct verify the effectiveness of each module in the proposed method.

## introduction automatic simultaneous translation (st) @xcite @xcite , a task in machine translation (mt), aims to output the target translation while reading the source sentence. the standard machine translation is a full-sentence mt, which waits for the complete source input and then starts translation. the huge latency caused by full-sentence mt is unacceptable in many realtime scenarios. on the contrary, st is widely used in real simultaneous speech translation scenarios, such as simultaneous interpretation, synchronized subtitles, and live broadcasting. previous methods @xcite for st are all evaluated on the existing full-sentence mt parallel corpus, ignoring the real speech translation scenario. in the real scene, the paradigm of simultaneous interpretation is automatic speech recognition (asr) → simultaneous translation (st) → text-to-speech synthesis (tts), in which these three parts are all carried out simultaneously. as a downstream task of simultaneous asr, the input of st is always not exactly correct and in the spoken language domain. thus, robustness and domain adaptability become two challenges for the st system. for robustness, since the input of the st system is asr result (streaming transcription), which is incremental and may be unsegmented or incorrectly segmented, the subword-level segmentation result @xcite of the streaming transcription seriously affect the st result. existing methods @xcite often remove the last token after segmentation to prevent it from being incomplete, which leads to a considerable increase in latency. for domain adaptability, the existing spoken language domain corpus is lacking, while the general domain corpus for mt and the spoken language domain corpus for st are quite different in terms of word order, punctuation and modal particles, so st needs to efficiently complete the domain adaption. in our system, we propose a char-level wait-k policy for simultaneous translation, which is more robust with streaming transcription input. besides, we apply data augmentation and combine two training methods to train the model to complete domain adaptation. specifically, the source of the char-level wait-k policy is a character sequence segmented according to characters, and the target still maintains subword-level segmentation and bpe operations @xcite . when decoding, tokenization of streaming transcription input streaming transcription standard wait-2 standard wait-2 remove last token 1 : an example of the tokenization result of standard wait-k, standard wait-k+remove last token and charlevel wait-k, when dealing with streaming transcription input (take @xmath0. red mark: the source prefix changes during streaming input. green mark: no input in consecutive steps since the last token is removed. the char-level wait-k policy first waits for k source characters, then alternately reads a character, and outputs a target subword. in the streaming transcription track for the @xmath1, we evaluate the proposed method on the real speech corpus @xcite . our method exceeds the baseline model at all latency and performs more prominently at lower latency. our contributions can be summarized as follows: ## task description we participated in the streaming transcription input track of the chinese-english translation task of autosimtrans 2021 ## background our system is based on a variant of wait-k policy @xcite , so we first briefly introduce waitk policy and its training method. wait-k policy refers to waiting for k source tokens first, and then reading and writing alternately, i.e., the output always delays k tokens after the input. as shown by 'standard wait-k policy' in figure 1 , if @xmath2, the first target token was output after reading 2 source tokens, and then output a target token as soon as a source token is read. define g (t) as a monotonic non-decreasing function of t, which represents the number of source tokens read in when outputting the target token y t . for the wait-k policy, g (t) is calculated as: wait-k policy is trained with "prefix-to-prefix" framework. in "prefix-to-prefix" framework, when generating the t th target word, the source tokens participating in encoder is limited to less than g (t). ## methods to improve the robustness and domain adaptability of st, we enhance our system from read / write policy, data processing and training methods respectively. ## related work previous st methods are mainly divided into precise read / write policy and stronger predictive ability. for read / write policy, early policies used segmented translation, and applied full sentence translation to each segment @xcite @xcite . @xcite trained an agent through reinforcement learning to decide read / write. @xcite proposed static-rw, which first performing s's reads, then alternately performing rw 's writes and reads. @xcite proposed wait-k policy, wherein first reads k tokens and then begin synchronizing write and read. wait-k policy has achieved remarkable performance because it is easy to train and stable, and is widely used in simultaneous translation. zheng et al. (2019a) generated the gold read / write sequence of input sentence by rules, and then trained an agent with the input sentences and gold read / write sequence. @xcite introduces a "delay" token {ε} into the target vocabulary to read one more token. arivazhagan et al. (2019 ) proposed milk, which uses a bernoulli distribution variable to determine whether to output. ma et al. (2020) proposed mma, the implementation of milk based on transformer. zheng et al. (2020) proposed a decoding policy that uses multiple fixed models to accomplish adaptive decoding. zhang et al. (2020a) propose a novel adaptive segmentation policy for st. for predicting future, @xcite applied pattern recognition to predict verbs in advance. @xcite used a markov chain to predict the next word and final verb. @xcite predict unseen syntactic constituents to help generate complete parse trees and perform syntax-based simultaneous translation. @xcite added a predict operation to the agent based on @xcite , predicting the next word as an additional input. elbayad et al. (2020) although the previous methods performed well, they were all evaluated on the traditional mt corpus instead of the real streaming spoken language corpus. therefore, the previous methods all ignore the robustness and domain adaptation of the st model in the face of real streaming input. our method bridgs the gap between the mt corpus and the streaming spoken language domain input, and is more robust and adaptable to the spoken language domain. ## conclusion and future work our submitted system won the first place in au-tosimtrans 2021, which is described in this paper. for streaming transcription input from the real scenarios, our proposed char-level wait-k policy is more robust than standard subword-level wait-k. besides, we also propose two data processing operations to improve the spoken language domain adaptability. for training, we combine two existing training methods that have been proven effective. the experiment on the data provided by the organizer proves the superiority of our method, especially at low latency. in this competition, we implemented the charlevel wait-k policy on the chinese source. for some language pairs with a large length ratio between the source (char) and the target (bpe), we can read multiple characters at each step to prevent the issue caused by the excessively long char-level source. we put the char-level simultaneous translation on other languages (such as german and english) for both fixed and adaptive policy into our future work. baigong zheng, renjie zheng, mingbo ma, and liang huang. 2019b. simultaneous translation with flexible policy via restricted imitation learning. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 5816-5822, florence, italy. association for computational linguistics.