1. 第一种,作者通过有标注的训练集的y和x构造一个correction-to-error 的mapping,这个mapping保存了当前token可以被替换的词和改词所在的句子。然后,为了保证多样性和语义上比较接近吗,作者这里选择使用当前token的句子和所有候选句子的编辑距离相似度来计算权重,也就是这里的权重wi来选择句子,如果两个句子之间相似度...
A Grammatical Error Correction Model for English Essay Words in Colleges Using Natural Language ProcessingENGLISH languageSENTENCES (Grammar)STATISTICAL learningNOUNSNATURAL language processingSPELLING errorsNatural language processing technology is a theory and approach for exploring and develop...
因为 less fluent sentence 是由 error generation model (由error-corrected数据训练得到)生成,因此不会改变原始文本含义,对此,文章认为可以将生成的文本和 corrected 句子组成句子对,用于训练 error correction 模型。 具体来说,首先利用 corrected 文本作为输入,error作为target句子训练error generation模型;然后将corrected...
Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GE...
Shallow Aggressive Decoding with BART (12+2), single model (beam=1) (Sun et al., ACL 2021)66.4Instantaneous Grammatical Error Correction with Shallow Aggressive DecodingOfficial Sequence tagging + token-level transformations + two-stage fine-tuning, DeBERTa (Mesham et al., EACL 2023)66.06An Ext...
Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected...
An implementation of transformer-based language model for sentence rewriting tasks such as summarization, simplification, and grammatical error correction. language-modelingtext-summarizationnlp-machine-learningtext-simplificationgrammatical-error-correction ...
To solve the Grammatical Error Correction (GEC) problem , a mapping between a source sequence and a target one is needed, where the two differ only on few spans. For this reason, the attention has been shifted to the non-autoregressive or sequence tagging models. In which, the GEC has be...
PKU uses a character-based MT model to deal with this problem. Besides, they propose a preprocessing module for the correction of spelling errors. First, the error detection is based on the binary features including cooccurrence probability, mutual information and chi-square test. Then confusion ...
This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC). The answer to this question is not as straightforward as one might expect because the previous common me...