Simultaneous Machine Translation with Tailored Reference (2023)

Abstract: Simultaneous machine translation (SiMT) generates translation while reading the whole source sentence. However, existing SiMT models are typically trained using the same reference disregarding the varying amounts of available source information at different latency. Training the model with ground-truth at low latency may introduce forced anticipations, whereas utilizing reference consistent with the source word order at high latency results in performance degradation. Consequently, it is crucial to train the SiMT model with appropriate reference that avoids forced anticipations during training while maintaining high quality. In this paper, we propose a novel method that provides tailored reference for the SiMT models trained at different latency by rephrasing the ground-truth. Specifically, we introduce the tailor, induced by reinforcement learning, to modify ground-truth to the tailored reference. The SiMT model is trained with the tailored reference and jointly optimized with the tailor to enhance performance. Importantly, our method is applicable to a wide range of current SiMT approaches. Experiments on three translation tasks demonstrate that our method achieves state-of-the-art performance in both fixed and adaptive policies.

## introduction simultaneous machine translation (simt) @xcite @xcite generates the target sentence while reading in the source sentence. compared to full-sentence translation, it faces a greater challenge because it has to make trade-offs between latency and translation quality @xcite . in applications, it needs to meet the requirements of different latency tolerances, such as online conferences and real-time subtitles. therefore, the simt models trained at source sentence ground-truth tailored ref 看 电影 是 一项 愉快的 活动 。 (watch)(a movie) (is) (an) (enjoyable) (activity) it is an enjoyable activity to watch a movie . watching a movie is an enjoyable activity . ## background for a simt task, the model reads in the source sentence @xmath0, ..., x j ) with length j and generates the target sentence @xmath1, ..., y i ) with length i based on the policy. to describe the policy, we define the number of source tokens read in when translating y i as g i . then the policy can be represented as @xmath2, ..., g i ). therefore, the simt model can be trained by minimizing the cross-entropy loss: wait-k policy as the most widely used fixed policy, the model reads in k tokens first and then alternates writing and reading a token. it can be formalized as: hmt hidden markov transformer (hmt), which derives from the hidden markov model, is the current state-of-the-art simt model. it treats the translation policy g as hidden events and the target sentence y as observed events. during training, hmt learns when to generate translation by minimizing the negative log-likelihood of observed events over all possible policies: ctc ctc @xcite is applied in nonautoregressive translation (nat) @xcite due to its remarkable performance and no need for length predictor. ctc-based nat will generate a sequence containing repetitive and blank tokens first, and then reduce it to a normal sentence based on the collapse function γ -1 . during training, ctc will consider all possible sequences a, which can be reduced to y using function γ -1 : ## method in this section, we introduce the architecture of our model, which incorporates tailor into the simt model. to train the simt model with the tailor, we present a three-stage training method, in which the simt model benefits from training with tailored reference and is optimized together with the tailor. during inference, the simt model generates translation according to the policy. the details are introduced in the following subsections. ## analysis to gain a comprehensive understanding of our method, we conducted multiple analyses. all of the following results are reported on @xmath3. ## related work simultaneous machine translation (simt) generates translation while reading the source sentence. it requires a policy to determine the source information read when translating each target token, thus striking a balance between latency and translation quality. current research on simt mainly focuses on two areas: policy improvement and adjustment of the training method. for policy improvement, it aims to provide sufficient source information for translation while avoiding unnecessary latency. @xcite propose wait-k policy, which initially reads k tokens and alternates between writing and reading one token. @xcite enable each head to obtain the information with a different fixed latency and integrate the decisions of multiple heads for translation. however, the fixed policy cannot be flexibly adjusted based on context, resulting in suboptimal performance. @xcite allow each head to determine its own policy and make all heads decide on the translation. @xcite propose a generative framework, which uses a re-parameterized poisson prior to regularising the policy. @xcite propose a segmentation policy for the source input. @xcite model the simultaneous machine translation as a hidden markov model and achieve state-of-the-art performance. however, these methods are all trained with ground-truth, leading to forced predictions at low latency. for the adjustment of the training method, it wants to supplement the missing full-sentence information or cater to the requirements of latency. @xcite shorten the distance of source hidden states between the simt model and the full-sentence model. this makes the source hidden states implicitly embed future information, but encourages data-driven prediction. on the other hand, @xcite try to train the model with non-anticipatory reference, which can be effectively handled by the simt model at that latency. however, while non-anticipatory reference can alleviate forced predictions at low latency, it hinders performance improvement at high latency. therefore, we want to provide a tailored reference for the simt models trained at different latency. the tailored reference should avoid forced anticipations and exhibit high quality. in view of the good structure and superior performance of the non-autoregressive model @xcite , we utilize it to modify the ground-truth to the tailored reference. ## conclusion in this paper, we propose a novel method to provide a tailored reference for the training of simt model. experiments and extensive analyses demonstrate that our method achieves state-of-the-art performance in both fixed and adaptive policies and effectively reduces the hallucinations in translation.