Adapting Neural Machine Translation for Automatic Post-Editing (2021)

Abstract: Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT’21 Automatic Post-Editing (APE) English-German (En-De) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT’21 test set.

## introduction automatic post-editing (ape) is the task of automatically correcting machine translation (mt) outputs. along with fixing systematic errors in mt outputs, ape models can adapt general purpose mt systems to new domains and provide better translations to reduce the human post-editing effort @xcite . ape has seen significant progress with transformer based models @xcite @xcite dominating the landscape as opposed to the earlier statistical machine translation (smt) based models @xcite and rnn based sequence-tosequence models @xcite . to track this progress, wmt has been conducting ape shared tasks since 2015 on different data domains and language pairs @xcite @xcite @xcite . wmt 2021's shared task focused on english-german and english-chinese language pairs. we participated in the english-german sub-task and describe our submission in this paper. participants * work done as intern at amazon prime video were provided a training set with 7000 instances and a development set with 1000 instances. each dataset consisted of source, machine-translation, post-edit triplets. the source sentences came from the english wikipedia, the mt outputs were generated with a black-box state-of-the-art mt system and the post-edits were created by professional translators correcting mt outputs. the test set consisted of 1000 pairs of source and mt outputs for which the participants had to submit the post-edits generated by their systems. the task organisers provided two additional synthetic post-editing datasets -'artificial training data' @xcite and 'escape corpus' @xcite and permitted using additional data to train the model. ter scores @xcite and bleu @xcite scores were used as primary and secondary evaluation metrics respectively. last year's entries primarily focused on transfer learning @xcite @xcite and novel data augmentation techniques @xcite . the winning submission @xcite was based on finetuning a pre-trained machine translation model for the ape task. we take a similar line of approach by leveraging an existing state-of-the-art machine translation model. we first fine-tune an mt model on wikimatrix @xcite -a mined bitext from wikipedia -to bridge the domain gap, followed by further tuning to the ape task with post-editing samples. to deal with the limited training data, we exploit ape data from the previous editions of the wmt shared tasks. we describe the details of our experiments in section 3 with gains and observations from individual tuning steps mentioned above. ## related work the last year's wmt'20 ape shared task saw methods using transfer learning with data augmentation techniques perform well. @xcite fine-tune state-of-the-art transformer-based mt system on ape data using bottleneck adapter layers @xcite to avoid overfitting. they additionally use outputs from an external mt system as input to the model and converged to ensembling to achieve 66.89 bleu score on the wmt'20 development set to make it to the top of the final leaderboard. data augmentation techniques where post-edits are synthesized to augment human-edited data was shown to be effective in the last year's submissions for addressing the training data limitation. however, data augmentation must be done carefully to prevent a mismatch between the error distributions in gold and synthetic data @xcite . @xcite use data augmentation along with dual conditional cross-entropy model @xcite ) based filtering to ensure data quality, model adaptation to target domain, and ensembling to achieve 56.06 bleu on the development set and the second rank on the leaderboard. similarly, @xcite performed data augmentation by creating a novel noising scheme to synthesize four kinds of errors for ape training, namely, insertion, deletion, substitution and shifting/reordering noise to attain 53.77 bleu score. the other submissions to the wmt'20 task used variations of the language models to generate edits. @xcite trained a model by jointly optimizing losses for masked language and translation language models while lee (2020) tailored a language model to make corrections by replacing poor quality words to improve the overall sentence-level quality. these two submissions were able to get 55.67 and 53.82 bleu scores respectively on the wmt'20 development set. in comparison, our model is a pre-trained mt model adapted to the target domain and further fine-tuned on the ape data. these improvements give us about five absolute points gain over the no post-editing baseline (that returns mt output without changes) on the bleu score to arrive at 55.85 which is competitive with all but one of last year's submissions on the wmt'20 development set. ## method we describe our baseline model followed by the details of domain and task adaptation in this section. ## results and conclusion we report the results of our model on the wmt'21 development and test set. we use bleu scores @xcite foot_1 for quality estimates relative to a human reference and ter scores @xcite for quantifying human post-editing effort. we report improvements over the do nothing baseline. this baseline refers to the system that returns the base machine translation output as the post-edit without any changes. we submitted the best performing single model and the ensemble model in further extending this work, we wish to study more carefully the impact of adaptation by switching the order of domain and task adaptation, effect of noise in training sample by tuning threshold @xcite , and evaluate if synthetic data can be selectively augmented for greater metric gains.