Коррекция грамматических ошибок

AbstractThis paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains …...

We present a novel beam-search decoder for grammatical error correction. The decoder iteratively generates new hypothesis correc- tions from current hypotheses and scores them based on features of grammatical correctness …...

We propose a joint inference algorithm for grammatical error correction. Different from most previous work where different error types are corrected independently, our proposed inference process considers all possible errors …...

State-of-the-art systems for grammatical er- ror correction are based on a collection of independently-trained models for specific er- rors. Such models ignore linguistic interac- tions at the sentence level and …...

AbstractThis paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system. We do this by analyzing the Illinois system that placed first among seventeen teams …...

Different approaches to high-quality grammatical error correction have been proposed recently, many of which have their own strengths and weaknesses. Most of these approaches are based on classi- fication or …...

An important aspect for the task of grammat- ical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite …...

Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error cor- rection methods that employ statistical ma- chine translation (SMT) have been …...

We focus on two leading state-of-the-art approaches to grammatical error correc- tion – machine learning classification and machine translation. Based on the com- parative study of the two learning frame- …...

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical er- ror correction. Current approaches gener- ally focus on relatively shallow semantics …...

AbstractThe field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, …...

Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing gram- matical edits that are correct but not in the …...

AbstractGrammatical error correction (GEC) systems strive to correct both global errors inword order and usage, and local errors inspelling and inflection. Further developing upon recent work on neural machine translation, …...

AbstractSeveral metrics have been proposed for evaluating grammatical error correction (GEC) systems based on grammaticality, fluency, and adequacy of the output sentences. Previous studies of the correlation of these metrics …...

AbstractPreviously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural …...

AbstractGrammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. …...

AbstractThis study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single commonly applied corpus: the CoNLL-2014 …...

AbstractThis paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks. Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI …...

AbstractUntil now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text …...

AbstractNeural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged …...

AbstractWe present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are based on the popular …...

AbstractExisting approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of …...

AbstractRecent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art …...

AbstractIn this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages …...

AbstractRecent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through …...

AbstractThe development of effective NLP tools for the L2 classroom depends largely on the availability of large annotated corpora of language learner text. While annotated learner corpora of English are …...

AbstractThe lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC). As a complementary new resource …...

AbstractMost recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data …...

AbstractA sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the …...

AbstractThe incorporation of data augmentation method in grammatical error correction task has attracted much attention. However, existing data augmentation methods mainly apply noise to tokens, which leads to the lack …...

AbstractWe propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese …...

AbstractWe propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction …...

AbstractLocal sequence transduction (LST) tasks are sequence transduction tasks where there exists massive overlapping between the source and target sequences, such as grammatical error correction and spell or OCR correction. …...

AbstractThis paper is concerned with semantic parsing for English as a second language (ESL). Motivated by the theoretical emphasis on the learning challenges that occur at the syntax-semantics interface during …...

AbstractWe propose Seq2Edits, an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts. In this approach, each …...

AbstractWe propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are …...

AbstractStudies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC …...

AbstractThis paper presents performance measures for grammatical error correction which take into account the difficulty of error correction. To the best of our knowledge, no conventional measure has such functionality …...

There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data. How- ever, these approaches suffer from several is- sues that make them …...

In Grammatical Error Correction (GEC), se- quence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are …...

This study proposes a new supervised atten- tion mechanism for grammatical error correc- tion, which is trained to focus the encoder- decoder attention to each word in the corrected sentence …...

AbstractStandard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we …...

AbstractIn this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency …...

AbstractIt is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics …...

AbstractGrammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has …...

AbstractIn this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new …...

AbstractGrammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect …...

AbstractIn this paper, we propose a system combination method for grammatical error correction (GEC), based on nonlinear integer programming (IP). Our method optimizes a novel F score objective based on …...

AbstractWhole word masking (WWM), which masks all subwords corresponding to a word at once, makes a better English BERT model. For the Chinese language, however, there is no subword because …...

AbstractIn grammatical error correction (GEC), automatic evaluation is considered as an important factor for research and development of GEC systems. Previous studies on automatic evaluation have shown that quality estimation …...

Abstract“Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, …...

AbstractIn this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple …...

AbstractThere has been much recent progress in natural language processing, and grammatical error correction (GEC) is no exception. We found that state-of-the-art GEC systems (T5 and GECToR) outperform humans by …...

AbstractIterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document. Most recent research has focused on …...

AbstractGrammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC models mainly aim at improving accuracy, …...

AbstractThis paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed …...

AbstractWe introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task. To keep the meaning of the input sentence unchanged, only a …...

AbstractThis study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data. QE models for GEC evaluations in prior …...

AbstractPretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and text summarization) due to their better correlation with …...

AbstractGrammatical 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. …...

AbstractWe extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. Our approach improves …...

AbstractGrammatical error correction (GEC) is the task of correcting typos, spelling, punctuation and grammatical issues in text. Approaching the problem as a sequence-to-sequence task, we compare the use of a …...

AbstractGrammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models. However, approaches of this class are inherently slow due to one-by-one token …...

AbstractThe most popular approach in grammatical error correction (GEC) is based on sequence-to-sequence (seq2seq) models. Similar to other autoregressive generation tasks, seq2seq GEC also faces the exposure bias problem, i.e., …...