Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing (2025)

Abstract: Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing. In this paper, we propose a novel technique to mitigate over-correction by incorporating word-level Quality Estimation (QE) information during the decoding process. This method is architecture-agnostic, making it adaptable to any APE system, regardless of the underlying model or training approach. Our experiments on English-German, English-Hindi, and English-Marathi language pairs show the proposed approach yields significant improvements over their corresponding baseline APE systems, with TER gains of 0.65, 1.86, and 1.44 points, respectively. These results underscore the complementary relationship between QE and APE tasks and highlight the effectiveness of integrating QE information to reduce over-correction in APE systems.

## introduction automatic post-editing (ape) focuses on developing computational approaches to improve machine translation (mt) system-generated output by following the principle of minimal editing @xcite . along with the shift in the field of mt research-from statistical to neural approaches, research within ape has observed a similar trend-towards neural ape systems @xcite @xcite . the need for large ape datasets for training neural ape models is addressed by generating artificial triplets @xcite @xcite . however, unlike real (human post-edited) ape triplets, these do not follow the minimality principle, leading to distributional differences @xcite . despite training on synthetic data and fine-tuning with real data, current ape systems face over-correction issues, primarily due to the size imbalance between synthetic and real data @xcite . while strategies like optimizing data selection, data augmentation, and model architecture have addressed ape over-correction, mitigating it at the decoding stage remains underexplored @xcite . focusing on other stages limits the applicability across different ape systems. motivated by this, we propose an over-correction mitigation method using an external quality estimation (qe) signal during decoding, applicable to any black-box ape system. our contribution is: ## related work there are multiple attempts to curtail the overcorrection at different stages of ape development. @xcite ; @xcite focus on data by selecting training samples that may prevent ape from facing the over-correction, augmentation with triplets containing the same translations and post-edits, and weighing training samples with perplexity-based scoring to limit their contribution to learning the ape model. junczys-dowmunt and grundkiewicz (2017) modify their ape architecture using monotonic hard attention to improve translation faithfulness. @xcite use task-specific loss based on attention scores to reward ape hypothesis words present in the original translation. tebbifakhr et al. (2019) train a classifier to predict postediting effort and prepend its output to source and translation sequences. tan et al. (2017) train separate ape models and use a qe system to rank their outputs. lee (2020a); deoghare and bhattacharyya (2022); yu et al. (2023) mitigate over-correction by reverting to the original translation based on qe speculation. chatterjee et al. (2018b) incorporate word-level qe information into the decoder to guide minimal edits. deoghare et al. (2023b) adopt a multitask approach, jointly training on qe and ape tasks to reduce over-correction. deguchi et al. (2024) use a detector-correction framework that first predicts the type of edit operation each translation token should undergo, and then the post-edit is generated based on this information. we find only a few attempts at handling over-correction at the decoding stage. junczys-dowmunt and grundkiewicz (2016) introduce a 'post-editing penalty' during decoding to prevent generating tokens not present in the input, applying it in an ensemble framework to one model. chatterjee et al. (2017) re-rank ape hypotheses based on precision and recall using shallow features like insertions, deletions, and length ratio, rewarding those closer to the original translation. lopes et al. (2019) impose a soft penalty for new tokens not in the inputs. lee et al. (2022) experiment with various decoding methods to generate artificial ape triplets. ## methodology we use an extension of beam search, called grid beam search @xcite , to perform decoding. while it is originally used for neural interactive-predictive translations and for mt do-main adaptation, we adopt the decoding technique for ape. to mitigate the ape over-correction, we explicitly provide information about correct translation segments during the decoding through finegrained word-level qe signals. ## experimental setup this section details the different experiments undertaken to assess the effectiveness of the proposed decoding technique. we use the same datasets, architecture, data augmentation, and preprocessing and also follow the same training approach as described by @xcite for training the ape models to enable direct comparison. appendix c details the english-german, english-hindi, and english-marathi datasets used for the experiments. ## results and discussion we perform the experiments on english-german (en-de), english-hindi (en-hi), and english-marathi (en-mr) pairs, each of which offers a different level of task difficulty due to different linguistic properties, varied amounts of real and synthetic datasets, and 'do nothing' baselines with different complexities. we use ter @xcite and bleu @xcite as primary and secondary evaluation metrics, respectively. kindly refer to appendix f for the bleu scores. a relatively large difference between standalone-ape + gbs (token) and standalone-ape + gbs (word) experiments for en-hi, en-mr pairs, and en-de pair hints the noise illusion goes up when target languages are morphologically richer. as we observe consistently better results in the case of standalone-ape + gbs (word) experiment, further experiments are performed by using word-based matching for enforcing constraints during the gbs decoding. a comparison between different decoding techniques and the proposed technique is depicted in on the other hand, a smaller difference in improvements between the two techniques (qe-ape + gbs vs qe-ape + bs) when the underlying ape system is jointly trained on qe and ape tasks underlines that the implicit knowledge of the qe tasks helps the model perform ape. yet, we can conjecture from the better performance with the use of the proposed method over the standard beam search that a loose coupling of qe with ape but with explicit information about the translation segment quality has the potential to improve an ape system developed through the stronger qe and ape coupling. in both cases, the difference between the proposed technique with oracle and non-oracle wordlevel qe information underscores the need for better word-level qe systems. we additionally perform experiments with other popular decoding techniques like greedy, sampling, and top-k sampling for completeness. the standalone-ape model is used in these experiments. the results show that the top-k sampling decoding performs similarly to the beam search decoding. the reported results are with the best k values for each pair (en-de: 25, en-hi: 30, en-mr: 25) as per empirical observations. comparison with existing techniques we also compare our proposed approach with the work of lopes et al. (2019), who apply a soft penalty during decoding if ape generates tokens that are not present in either source or translation vocabularies. for this experiment too, we use the standalone ape (standalone-ape) system. while we observe significant improvements in the case of en-hi and en-mr pairs, the technique shows limited gains when compared to the proposed approach, suggesting it is more beneficial to inform ape about what to generate than what not to generate since nmt outputs are usually of high quality and require minimal editing. furthermore, even though the key aim of this work is to develop an over-correction mitigation technique that could be integrated with any neural network-based ape system, we still compare our proposed technique with existing work that uses the edit operation or qe information at the time of training the ape models. due to the experimental setup consistency between this work and of @xcite , the standalone-ape + bs experiment represents their technique. its comparison with the standalone-ape + gbs suggests qe-assisted constrained decoding could be more robust in handling the over-correction than relying on the implicit learning of the qe information by the model. similarly, the comparison with the technique proposed by @xcite that relies on the edit operations prediction capabilities of the model shows comparable performance improvements with the performance of our technique. ## conclusion and future work the proposed decoding technique in this work has demonstrated its effectiveness in enhancing the quality of ape outputs by enforcing the generation of provided correct translation segments during decoding. these segments are extracted with the help of a word-level qe system, which offers fine-grained information about translation quality. through experiments on three language pairs, en-de, en-hi, and en-mr, the technique achieved improvements of 0.87 to 2.28 ter points over baseline ape systems. notably, the superior performance of standalone ape systems using the proposed decoding method compared to qe-ape systems with traditional beam search decoding underscores the technique's ability to reduce overcorrection. this result also suggests that injecting word-level qe information exclusively at the decoding stage is more effective than embedding it implicitly through joint qe and ape training. however, the relatively smaller gains when applying the technique to qe-ape systems imply that incorporating explicit qe information at the decoding stage addresses remaining gaps even after joint training with qe and ape. in the future, we would like to investigate the impact of the quality of a word-level qe system on the proposed decoding technique.