Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020)

Abstract: With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules – quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English–German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.

## introduction the explosive advances in the sequence to sequence model @xcite @xcite enable the deep learning based neural machine translation (nmt) to approximate and even achieve the human parity in some specific language pairs and scenarios. instead of translating from scratch by human translators, a new translation paradigm has emerged: computer assisted translation (cat) system, which includes the machine translation and human post-editing. the post-editing is the process whereby humans amend machine-generated translations to achieve ⇤ indicates equal contribution. † indicates corresponding author. an acceptable final product. practically, the estimated average translation time can be reduced by 17.4% (from 1957.4 to 1617.7 seconds per text) @xcite . however, utilizing nmt poses two key challenges. first, the neural machine translation quality still continues to vary a great deal across different domains or genres, more or less in proportion to the availability of paralleled training corpora. second, the zero tolerance policy is a common choice in the vast majority of important applications. for example, when business legal documents are translated, even a single incorrect word could bring serious financial or property losses. therefore, the subsequent human post-editing is indispensable in situations like this. unfortunately, while nmt systems saves time by providing the preliminary translations, the time spent on error corrections by humans @xcite remains substantial to the extent that it offsets the efficiency gained by the nmt systems. in this paper, we explore automatic post-editing (ape) in the deep learning framework. specifically, we adopt an imitation learning approach, where our model first screens the translation candidates by quality prediction and then decides whether to post edit with the generation or the atomic operation method. starting with a wide range of features used in the cat system, we carefully analyze the human post-editing results to narrow down our framework design into three key modules: quality estimation (qe), generative post-editing and atomic operation post-editing. these modules are tightly integrated into the transformer neural networks @xcite . our main innovation is a hierarchical model with two modular post-editing algorithms which are conditionally used based on a novel fine-grained quality estimation model. for each machine translation, our model i) runs the qe model to predict the detailed token level errors, which will be further summarized as an overall quality score to decide whether the machine translation quality is high or not, and ii) conditional on the previous decision, employs the atomic operation post-editing algorithm on the high quality sentence or the generative model to rephrase the translation for the low one. we examine our approach on the public english-german dataset from wmt 1 2017 ape shared task. our system outperforms the top ranked methods in both bleu and ter metrics. in addition, following a standard human evaluation process aimed at achieving impartiality with respect to the efficiency of cat system, we ask several certified translators to edit the machine translation outputs with or without our ape assistance. evaluation results show that our system significantly improves translators' efficiency. ## related work our work relates to and builds on several intertwined threads of research in machine translation, including qe and ape. we briefly survey the traditional methods and differentiate our approach. ## model and objective in order to achieve the automatic post-editing goal, it is essential for the model to find the exact errors appearing in the machine translation and learn how to fix them. breaking the problem into several subtasks, our proposed pipeline includes three major models as figure 1 . by skipping the pre-training temporarily, the first step is to investigate the finegrained quality estimation model with respect to the source text and machine translated text. its output will provide a fine-grained quality estimation of the machine translation. based on the corresponding quality, an atomic ape or a generative ape model will be called for further processing. encoder memory encoder source masked target or pe masked tokens pre-training decoder encoder memory encoder source machine translation right-shifted pe pe generative ape encoder memory encoder source machine translation qe tags fine-grained qe encoder memory encoder source atomic operation ape placeholder inserter pe qe tags + machine translation then, high quality machine translation will proceed with atomic ape model for minor fix, while the low quality machine translation will go through a generative ape model for completely rephrasing. note that the model parameters are shared for three steps w.r.t. encoder and memory encoder. detailed computational graph can refer to figure 2 . ## experiments on our proposed model we verify the validity and efficiency of the proposed ape model by conducting a series of ape experiments and human evaluation on wmt'17 ape dataset. for convenience, we denote the generative post-editing model as gm, the atomic operation post-editing model as aom, and the final hierarchical model as hm in this section. ## conclusion in this paper, we propose a hierarchical model that utilizes the fine-grained word-level qe prediction to select one of the two ape models we propose to generate better translations automatically, which shows a state-of-the-art performance. in particular, we design a dynamic deep learning model using imitation learning, which intuitively mimics the editing behaviors of human translators. our hierarchical model is not a standard ensemble model in the conventional sense. we merely share the parameters of different modules to accomplish different objectives, including qe, aom and gm. our experimental findings show that if the characteristics of errors in the machine translation can be accurately simulated, it is highly likely that mt output can be automatically refined by the ape model. towards this end, we conduct a rigorous comparison of the machine translation and automatic post-editing based manual post-editing tasks, and it is observed that the latter can significantly increase the efficiency of post-editing.