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Grammatical Error Correction with Contrastive Learning in Low Error Density Domains

تصحيح الأخطاء النحوية مع التعلم المتعاقل في مجالات كثافة خطأ منخفضة

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.



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GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level. It is based on two state-of-the-art models for gramm ar error correction and sentence ordering. GECko+ is available online as a web application that implements a pipeline combining the two models.
In this paper, we present a new method for training a writing improvement model adapted to the writer's first language (L1) that goes beyond grammatical error correction (GEC). Without using annotated training data, we rely solely on pre-trained lang uage models fine-tuned with parallel corpora of reference translation aligned with machine translation. We evaluate our model with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English. We show that our model is able to address specific L1-influenced writing and more complex linguistic phenomena than existing methods, outperforming what a state-of-the-art GEC system can achieve in this regard. Our code and data are open to other researchers.
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulating mistakes made by second language learners, and produces a wider rang e of non-native style language in comparison to a state-of-the-art baseline model. We carry out quantitative and qualitative evaluation. Our method is shown to outperform the baseline on data with a high proportion of errors.
In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELERRANT. ERRANT functions as a rule-based error type classifier and was used as the main evaluation tool of the systems parti cipating in the BEA-2019 (Bryant et al., 2019) shared task. Here, we discuss grammatical and morphological differences between English and Greek and how these differences affected the development of ELERRANT. We also introduce the first Greek Native Corpus (GNC) and the Greek WikiEdits Corpus (GWE), two new evaluation datasets with errors from native Greek learners and Wikipedia Talk Pages edits respectively. These two datasets are used for the evaluation of ELERRANT. This paper is a sole fragment of a bigger picture which illustrates the attempt to solve the problem of low-resource languages in NLP, in our case Greek.
Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Studies on GEC have proposed several methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences. Currently , a mainstream approach to generate pseudo data is back-translation (BT). Most previous studies using BT have employed the same architecture for both the GEC and BT models. However, GEC models have different correction tendencies depending on the architecture of their models. Thus, in this study, we compare the correction tendencies of GEC models trained on pseudo data generated by three BT models with different architectures, namely, Transformer, CNN, and LSTM. The results confirm that the correction tendencies for each error type are different for every BT model. In addition, we investigate the correction tendencies when using a combination of pseudo data generated by different BT models. As a result, we find that the combination of different BT models improves or interpolates the performance of each error type compared with using a single BT model with different seeds.

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