Towards Making the Most of Pre-trained Translation Model for Quality Estimation (2022)

Abstract: “Machine translation quality estimation (QE) aims to evaluate the quality of machine translation automatically without relying on any reference. One common practice is applying the translation model as a feature extractor. However, there exist several discrepancies between the translation model and the QE model. The translation model is trained in an autoregressive manner, while the QE model is performed in a non-autoregressive manner. Besides, the translation model only learns to model human-crafted parallel data, while the QE model needs to model machinetranslated noisy data. In order to bridge these discrepancies, we propose two strategies to posttrain the translation model, namely Conditional Masked Language Modeling (CMLM) and Denoising Restoration (DR). Specifically, CMLM learns to predict masked tokens at the target side conditioned on the source sentence. DR firstly introduces noise to the target side of parallel data, and the model is trained to detect and recover the introduced noise. Both strategies can adapt the pre-trained translation model to the QE-style prediction task. Experimental results show that our model achieves impressive results, significantly outperforming the baseline model, verifying the effectiveness of our proposed methods.”

## introduction machine translation has always been the hotspot and focus of research. compared with traditional methods, neural machine translation (nmt) has achieved great success. however, current translation systems are still not perfect to meet the real-world applications without human post-editing. therefore, to carry out risk assessment and quality control for machine translation, how to evaluate the quality of machine translation is also an important problem. quality estimation (qe) aims to predict the quality of machine translation automatically without relying on reference. compared with commonly used machine translation metrics such as bleu @xcite and meteor @xcite , qe can be applicable to the case where reference translations are unavailable. it has a wide range of applications in post-editing and quality control for machine translation. the biggest challenge for qe is data scarcity. since qe data is often limited in size, it is natural to transfer bilingual knowledge from parallel data to the qe task. one well-known framework for this knowledge transfer is the predictor-estimator framework, in which the predictor is trained on large parallel data and used to extract features, and the estimator will make quality estimation based on features provided by the predictor. the predictor is usually a machine translation model, which can hopefully capture the alignment or semantic information of the source and the target in a pair. @xcite first proposed to use an rnn-based machine translation model as the feature extractor, to leverage massive parallel data to alleviate the sparsity of annotated qe data. @xcite employed a pre-trained translation model as the predictor and added pseudo-pe information to predict translation quality. however, there are two discrepancies between machine translation and quality prediction, which impedes the nmt model to be directly adopted for feature extraction. i) translation task is usually a language generation task trained in an autoregressive manner, where each token is only conditioned on previous tokens unidirectionally. but qe is a language understanding task performed in a nonautoregressive manner, therefore each token could attend to the whole context bidirectionally. ii) the predictor is trained on human-crafted parallel data and only learns to model the alignment between correct translation pairs. however, the qe task needs to model machine-translated, imperfect translation pairs. both discrepancies may hinder the adaptation of the pre-trained nmt model to the downstream qe task, leading a degradation of model performance @xcite . in this paper, we propose two strategies to alleviate the discrepancies, named as conditional mask language modeling (cmlm) and denoising restoration (dr). both strategies are applied to the pretrained nmt model and can be deemed as a post-training phase. the cmlm is to train the nmt model to recover the masked tokens at the target side in a non-autoregressive manner, where each token can attend to the whole target sequence bidirectionally. furthermore, the dr first generates erroneous translation by performing conditionally masked language modeling, and then trains the nmt model to detect the introduced noise and recover the target sequence, which is also performed in a non-autoregressive manner. both methods can adapt the autoregressive nmt model to non-autoregressive qe prediction. moreover, compared with cmlm, dr removes the introduction of @xcite token (which may also cause the discrepancy between pre-training and qe prediction). besides, adversarially using another model with knowledge distillation to generate noise could provide more natural and harder training samples, thereby pushing the translation model better model the semantic alignment between the imperfect translation and source sequence. after the post-training phase, the nmt model is better adapted to the quality prediction task, and can serve as a better feature extractor. our contributions can be summarized as follows: ## approach in this section, we first describe the nmt-based qe architecture, and then describe our proposed posttraining strategies. ## conclusion when applying the pre-trained machine translation model to feature extraction for qe, there are two discrepancies between the nmt model and the qe model. one is the difference in data quality, the other is the regressive behavior of the decoder. in this paper, we propose two strategies to adapt the neural machine translation model to qe task, namely conditional masked language modeling and denoising restoration. the cmlm adopts a mask-prediction task at the target side, which allows the model to learn context-based representations. moreover, the dr employs a noiser-restorer architecture, where the noiser is used to generate sentences with the same noise distribution as machine translation, then the restorer will detect and recover the introduced noise. compared with the original nmt model, our methods bridge the gaps between the nmt model and the qe model, making it more suitable for the qe task. the experimental results verify the effectiveness of our methods. the main work in this paper focuses on sentence-level task. intuitively, the discrepancy also exists on word-level quality estimation when applying the pre-trained nmt model, and our strategies could function without any adaptation. besides, enhancing the estimator can also improve qe performance, and we will leave this as our future work.