Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022)

Abstract: Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference. Previous studies mainly focus on the data augmentation approach to combat the exposure bias, which suffers from two drawbacks. First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections. Second, they ignore the interdependence between different types of corrections. In this paper, we propose a Type-Driven Multi-Turn Corrections approach for GEC. Using this approach, from each training instance, we additionally construct multiple training instances, each of which involves the correction of a specific type of errors. Then, we use these additionally-constructed training instances and the original one to train the model in turn. Experimental results and in-depth analysis show that our approach significantly benefits the model training. Particularly, our enhanced model achieves state-of-the-art single-model performance on English GEC benchmarks. We release our code at Github.

## introduction grammatical error correction (gec) aims at automatically detecting and correcting grammatical (and other related) errors in a text. it attracts much attention due to its practical applications in writing assistant @xcite , speech recognition systems @xcite @xcite etc. inspired by the success of neural machine translation (nmt), some models adopt the same paradigm, namely nmt-based models. they have been quite successful, especially with data augmentation approach @xcite @xcite @xcite . however, these models have been blamed for their inefficiency during inference @xcite . to tackle this issue, many researchers resort to the sequence-to-label (seq2label) formulation, achieving comparable or better performance with efficiency @xcite @xcite . despite their success, both nmt-based and seq2label models are trained by one-iteration learning, while correcting errors for multiple iterations during inference. as a consequence, they suffer from exposure bias and exhibit performance degrade @xcite @xcite . to deal with this issue, @xcite propose to generate fluency-boost pseudo instances as additional training data. besides, @xcite dynamically augment training data by introducing the predicted sentences with high error probabilities. however, the above-mentioned approaches construct pseudo data based on a gec model or an error-generation model, which extremely depends on the performance of these models. thus, the error distribution of pseudo data is biased and lacks diversity and practicality. moreover, they simply mix original and pseudo data to train models, which are unable to learn correcting errors progressively. furthermore, they ignore the interdependence between different types of errors, which intuitively plays an important role on gec. taking reference sentence: in my country there are few jobs because the economy is very bad . in this paper, we first conduct quantitative experiments to investigate the performance improvements of gec model with providing different types of error corrections. experimental results show that corrections of appending or replacing words first indeed benefit the corrections of other errors. furthermore, we propose a type-driven multi-turn corrections (tmtc) approach for gec. concretely, by correcting a certain type of errors with others unchanged, we construct an intermediate sentence for each training instance and pair it with its raw erroneous sentence and reference sentence respectively, forming two additional training instances. during the model training, using the former instance, we firstly guide the model to learn correcting the corresponding type of errors. then, using the latter instance, we teach the model to correct other types of errors with the help of previous corrections. overall, contributions of our work are three-fold: generally, there are two categories of models in gec: transformer-dominant nmt-based models @xcite @xcite @xcite and gectorleading seq2label models @xcite @xcite . the former models consider gec as a machine translation task, where the model is fed with the erroneous sentence and then output the corrected sentence token by token. by comparison, seq2label models are able to correct grammatical errors more efficiently and even better. among them, the gector models @xcite ) obtain remarkable performance. typically, they adopt a pre-trained language model as the encoder to learn word-level representations and utilize a softmax-based classifier to predict designed editing-action labels. since gec models may fail to completely correct a sentence through just one-iteration inference, some researchers resort to data augmentation that has been widely used in other nlp studies @xcite . for instance, @xcite propose to let the gec model infer iteratively and design a fluency boost learning approach. specifically, they establish new erroneousreference sentence pairs by pairing predicted less fluent sentences with their reference sentences during training. likewise, to solve the mismatches between training and inference of seq2label models, @xcite apply a confidence-based method to construct additional training data by pairing low-confidence sentences with reference sentences. note that these two methods also involve constructing pseudo data using sentences with partial errors. however, ours is still different from them in two aspects. first, these two methods simply mix their pseudo data with original data to still train models in a one-iteration learning manner. by contrast, we decompose the one-iteration corrections into multiple turns, so as to make the model aware of gradual corrections. second, these two methods ignore the interdependence between different types of errors, which is exploited by our proposed approach to enhance the model. ## background in this work, we choose gector @xcite as our basic gec model due to its efficiency and competitive performance. typically, it considers the gec task as a sequence-tolabel task, where the candidate editing-action labels mainly include @xmath0delete (to delete the current word), @xmath1replace_{t} (to replace the current word with the word t) and some elaborate g-transformation labels @xcite performing task-specific operations, such as @xmath2transform_case_capital (to change the case of the current word). on the whole, the gector model is composed of an encoder based on pre-trained language model and two linear classifiers: one for grammatical error detection (ged) and the other for gec. the encoder reads the erroneous sentence x @xmath9, x 2 , ..., x n and represent words with hidden states {h i } n @xmath10, which are fed into classifiers to predict the binary label sequence @xmath11, y 2 , ..., y n for ged and the editing-action label sequence @xmath12, t 2 , ..., t n for gec, respectively. formally, the losses of two classifiers can be formulated as it is worth noting that the gector model is trained to correct all errors in a one-iteration manner, while correcting errors in a multiple-iteration way during inference (at most 5 iterations). besides, there are three stages involved during the training of the gector model, as shown in ## effect of the interdependence between different types of corrections in this section, we conduct several groups of quantitative experiments to explore the interdependence dataset #instance stage pie-synthetic (awasthi et al., 2019) 9,000,000 i lang-8 @xcite 947,344 ii nucle @xcite 56,958 ii fce @xcite 34,490 ii w&i+locness @xcite 34,304 ii, iii we first train the gector model on stage ii only for efficiency. all training settings are the same to published parameters. 1 afterwards, we use the model to conduct corrections on the bea-2019 (w&i+locness) dev set and conll-2014 test set @xcite and their variants with some errors corrected manually. for simplicity, we only consider the three most frequent editingaction labels: @xmath3delete and @xmath4append_{t}/@xmath5replace_{t}, and words with other types of errors. if we want to investigate the influence of @xmath6append_{t} labels and denote them as d(append). then we manually correct all the errors which should be corrected by @xmath7delete and @xmath8delete" as target labels unchanged during the constructions of intermediate sentences. using additionally-constructed training instances involving these sentences, the trained model will be encouraged to focus on performing corrections first via $delete. using our approach, we adopt different objectives to successively train our model. specifically, notably, there remain some grammatical errors within intermediate sentences which not be learned by the model in the first turn. therefore, we omit the incorrect supervisal signals in the definition of l (1) c via an indicator function 1( * ), which is used to shield the effect of incorrect losses. however, because our additionally-constructed training instances contain less grammatical errors compared with original ones, which causes the trained model to correct less errors. to address this defect, we still use the original training instances to continuously train model in the third turn. formally, we finally, we use all training instances to continuously train our model with the following objective ## experiment 6.1 setup to ensure fair comparison, we train the gector models using the same training datasets and parameters as @xcite , and then evaluate them on the bea-2019 (w&i+locness) dev, test set and the conll-2014 test set. the details of the training data are listed in three stages of training. in this setting, we compare our enhanced models with more baselines under the setting of the single model, including the most related work, dual-boost @xcite , gector(gst) @xcite and the current best nmt-based model sad(12+2) @xcite . as reported in ## conclusion in this paper, we have firstly conducted quantitative experiments to explore the interdependence between different types of corrections, with the finding that performing some types of corrections such as appending or replacing words first help models to correct other errors. futhermore, we propose a type-driven multi-turn corrections (tmtc) approach for gec, which allows the trained model to be not only explicitly aware of the progressive corrections, but also exploit the interdependence between different types of corrections. extensive experiments show that our enhanced model is able to obtain comparable or better performance compared with the sota gec model. in the future, we plan to apply bidirectional decoding @xcite @xcite to further improve our approach. besides, inspired by the recent syntax-aware research @xcite , we will explore the interdependence between corrections from other perspectives for gec such as syntax.