Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation (2022)

Abstract: Simultaneous machine translation (SiMT) starts its translation before reading the whole source sentence and employs either fixed or adaptive policy to generate the target sentence. Compared to the fixed policy, the adaptive policy achieves better latency-quality tradeoffs by adopting a flexible translation policy. If the policy can evaluate rationality before taking action, the probability of incorrect actions will also decrease. However, previous methods lack evaluation of actions before taking them. In this paper, we propose a method of performing the adaptive policy via integrating post-evaluation into the fixed policy. Specifically, whenever a candidate token is generated, our model will evaluate the rationality of the next action by measuring the change in the source content. Our model will then take different actions based on the evaluation results. Experiments on three translation tasks show that our method can exceed strong baselines under all latency.

## introduction simultaneous machine translation (simt) @xcite @xcite starts translation before reading the whole source sentence. it seeks to achieve good latency-quality tradeoffs and is suitable for various scenarios with different latency tolerances. compared to full-sentence machine translation, simt is more challenging because it lacks partial source content in translation @xcite and needs to decide on translation policy additionally. the translation policy in simt directs the model to decide when to take read (i.e., read the next source token) or write (i.e., output the generated token) action, so as to ensure that the model has appropriate source content to translate the target however, the previous methods, including fixed and adaptive policies, lack evaluation before taking the next action. for fixed policy @xcite @xcite , the model generates translation according to the predefined translation rules. although it only relies on simple training methods, it cannot make full use of the context to decide an appropriate translation policy. for adaptive policy @xcite @xcite , the model can obtain better translation performance. but it needs complicated training methods to obtain translation policy and takes action immediately after making decisions, which usually does not guarantee the accuracy of actions. therefore, we attempt to explore some factors from the translation to reflect whether the action is correct, thereby introducing evaluation into trans-lation policy. the goal of translation is to convert sentences from the source language to the target language @xcite , so the source and target sentences should contain the same semantics (i.e., global equivalence). to ensure the faithfulness of translation @xcite , the source content that has already been translated should be semantically equivalent to the previously generated target tokens at each step (i.e., partial equivalence) @xcite . furthermore, by comparing the changes between adjacent steps, the increment of the source content being translated should be semantically equivalent to the current generated token (i.e., incremental equivalence). therefore, the rationality of the generated target token can be reflected by the increment of the source content being translated between adjacent steps, which can be used to evaluate the read and write actions. in this paper, we propose a method of performing the adaptive policy by integrating post-evaluation into the fixed policy, which directs the model to take read or write action based on the evaluation results. using partial equivalence, our model can recognize the translation degree of source tokens (i.e., the degree to which the source token has been translated), which represents how much the source content is translated at each step. then naturally, by virtue of incremental equivalence, the increment of translated source content can be regarded as the change in the translation degree of available source tokens. therefore, we can evaluate the action by measuring the change in translation degree. as shown in figure 1 , if the translation degree has significant changes after generating a candidate token, we think that the current generated token obtains enough source content, and thus write action should be taken. otherwise, the model should continue to take read actions to wait for the arrival of the required source tokens. experiments on wmt15 @xmath0. ## background transformer @xcite , which consists of encoder and decoder, is the most widely used neural machine translation model. given a source sentence @xmath1, ..., x i ), the encoder maps it into a sequence of hidden states @xmath2, ..., z i ). the decoder generates target hidden states @xmath3, ..., h m ) and predicts the target sentence @xmath4, ..., y m ) based on z autoregressively. our method is based on wait-k policy @xcite and capsule networks @xcite with guided dynamic routing @xcite , so we briefly introduce them. ## the proposed method the architecture of our method is shown in figure 2 . our method first guides the model to recognize the translation degree of available source tokens based on partial equivalence during training via the introduced gdr module. then based on the incremental equivalence between adjacent steps, our method utilizes the changes in translation degree to post-evaluate the rationality of the read and write actions and accordingly make corrections, thereby performing an adaptive policy during inference. besides, to enhance the robustness of the model in recognizing the translation degree during inference, our method applies a disturbed-path training based on the wait-k policy, which adds some disturbance to the translation policy during training. the details are introduced in the following sections in order. ## analysis to understand our proposed method, we conduct multiple analyses. all of the following results are reported on @xmath5. ## conclusion in this paper, we propose a new method of performing the adaptive policy by integrating postevaluation into the fixed policy to evaluate the rationality of the actions. besides, disturbed-path training is proposed to enhance the robustness of the model to recognize the translation degree on different translation paths. experiments show that our method outperforms the strong baselines under all latency and can recognize the translation degree on different paths accurately. furthermore, pe policy can enhance the sufficiency and necessity of translation paths to achieve better performance.