Detector–Corrector: Edit-Based Automatic Post Editing for Human Post Editing (2024)

Abstract: Post-editing is crucial in the real world because neural machine translation (NMT) sometimes makes errors.Automatic post-editing (APE) attempts to correct the outputs of an MT model for better translation quality.However, many APE models are based on sequence generation, and thus their decisions are harder to interpret for actual users.In this paper, we propose “detector–corrector”, an edit-based post-editing model, which breaks the editing process into two steps, error detection and error correction.The detector model tags each MT output token whether it should be corrected and/or reordered while the corrector model generates corrected words for the spans identified as errors by the detector.Experiments on the WMT’20 English–German and English–Chinese APE tasks showed that our detector–corrector improved the translation edit rate (TER) compared to the previous edit-based model and a black-box sequence-to-sequence APE model, in addition, our model is more explainable because it is based on edit operations.

## introduction neural machine translation (nmt) @xcite @xcite @xcite sometimes make errors @xcite , and postediting is crucial in the real world to correct the © 2024 the authors. this article is licensed under a creative commons 4.0 licence, no derivative works, attribution, cc-by-nd. figure 1 : overview of the post-editing process of our detector-corrector model. the detector tags as "jeden abend" is untranslated, "drink" and "i" should be reordered, etc. the corrector generates the word sequence for replacement and insertion. mis-translations. automatic post-editing (ape) attempts to correct and refine the translations generated by mt models (mt sentences) for better translation quality. however, many ape models are based on sequence generation @xcite @xcite @xcite , and their decision for correction is harder to interpret due to the black-box nature of the generation models. some prior work @xcite @xcite @xcite showed that edit-based models improve interpretability in monolingual text editing, e.g., grammatical error correction (gec), compared with sequence-to-sequence models. the ape task can be regarded as a text edit task in terms of rewriting mt sentences, but differs from general monolingual text editing tasks in that it uses cross-lingual information from source sentences, such as inserting untranslated words and re-ordering translation words. for example, if an editbased model cannot perform reordering, it is represented as deletion and insertion, which increases the number of edit operations and makes it harder for humans to interpret the edit. in this paper, we propose "detector-corrector", an edit-based post-editing model, in which the post-editing process is broken into two steps for assisting human post-editing: error detection and error correction. we designed our model after interviewing with professional translators regarding the post-editing process; specifically, they first spot errors and then make corrections, and omission errors are crucial for the editing process. the overview of our detector-corrector model is shown in figure 1 . the detector model, which extends a word-level quality estimation (qe) model, tags each mt output token as whether it should be corrected and/or reordered and identifies which source tokens are not translated in the mt sentence. then, the corrector model receives the annotated source and mt sentences and corrects words for each span identified as incorrect in the detector model. our corrector model can insert any number of spans of variable length. in addition, we propose data augmentation methods especially designed for the detector and corrector models to enhance each model, and lightweight iterative refinement to improve the inference speed. experiments on the wmt'20 english-german (en-de) and english-chinese (en-zh) ape tasks showed that our detector-corrector improved translation edit rate (ter) @xcite compared to not only an edit-based model @xcite but also a black-box sequence-tosequence model by 0.7 points in en-de and en-zh. moreover, our model is more explainable than sequence-to-sequence models because it is based on edit operations and it can be integrated into computer-aided translation tools @xcite . levenshtein transformer @xcite , a non-autoregressive transformer encoder-decoder model, predicts deletion, placeholder insertion, and word filling. it can be used for the ape task by rewriting an mt sentence, but it cannot represent reordering and detecting untranslated words. seq2edits @xcite ) edits an input text by span tagging and replacement prediction to improve interpretability for text-editing tasks. however, it is not suitable for the ape task because it only monotonically edits an mt output from left to right according to the tags and cannot perform reordering of spans or inserting missing words which often occur in erroneous translations. felix @xcite breaks down text editing into three components: tagging, reordering, and word in-filling. it performs tagging using a pre-trained encoder model like bert, reordering using a pointer network, and predicting words of replacement and insertion using a masked language model. however, it does not explicitly use source information. in addition, word insertion is predicted non-autoregressively; thus, the number of words to be inserted must be given in advance for the insertion operation, which is not trivial. edit5 @xcite uses the t5 (raffel et al., 2020) encoder-decoder and decomposes the editing process into (1) tagging that decides which tokens are kept, (2) reordering the input tokens, and (3) insertion that infills the missing tokens. unlike felix, edit5 uses the autoregressive t5 decoder for word prediction, allowing for variable length insertion. however, the positions that can be inserted depend on the special tokens used in pre-training of t5 for filling masked spans, e.g., <extra id 6> @xmath0; thus, the number of positions that can be inserted is limited to those observed in pre-training. ## conclusion we proposed "detector-corrector", the edit-based automatic post-editing (ape) model, which explains which words are wrong in mt sentences and how to correct them for human post-editors. experiments on the wmt'20 english-german and english-chinese ape tasks showed that our detector-corrector model provides the editing process and outperformed the previous edit-based model, levenshtein transformer, and a black-box sequence-to-sequence ape model in ter. in the future, we will further investigate what is needed to reduce the workload of human posteditors.