Correct Me If You Can: Learning from Error Corrections and Markings

A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction

Grammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences. Recently, neural machine translation systems have become popular approaches for this task. However, these methods lack the use of syntactic knowledge which plays an important role in the correction of grammatical errors. In this work, we propose a syntax-guided GEC model (SG-GEC) which adopts the graph attention mechanism to utilize the syntactic knowledge of dependency trees. Considering the dependency trees of the grammatically incorrect source sentences might provide incorrect syntactic…

Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction

We 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 (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with les…

基于编辑层面多数投票的过纠正缓解方法：用于基于大语言模型的语法错误修正

Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction

本文提出一种无需训练的推理方法，通过多候选生成模型进行编辑层面多数投票，缓解LLM在语法纠错中的过纠正问题，在多个语言基准测试中优于贪婪和MBR解码。

Type-Driven Multi-Turn Corrections for Grammatical Error Correction

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…

基于提示插入的受控生成用于语法纠错中的自然语言解释

Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction

本文提出基于提示插入的受控生成方法，通过自动提取并插入纠错点提示，引导大语言模型为语法纠错生成完整自然语言解释，并构建XGEC数据集验证其有效性。

请求对话中的错误纠正与提取

Error correction and extraction in request dialogs

本文提出一个对话系统组件，通过检测用户最后话语是否为倒数第二句的错误纠正，实现实体短语的修正与提取，并在修改的EPIC-KITCHENS-100数据集上达到96.40%和77.81%的准确率。

CECOR：面向事实错误纠正的修正导向合成数据构建

CECOR: Correction-oriented synthetic data construction for factual error correction

提出CECoR框架，通过分解与注入范式合成高质量训练数据，结合两阶段学习策略，有效提升多跳事实错误纠正的准确性和鲁棒性。

An Error-Guided Correction Model for Chinese Spelling Error Correction

Although existing neural network approaches have achieved great success on Chinese spelling correction, there is still room to improve. The model is required to avoid over-correction and to distinguish a correct token from its phonological and visually similar ones. In this paper, we propose an error-guided correction model (EGCM) to improve Chinese spelling correction. By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying…

OCR Error Correction Using Character Correction and Feature-Based Word Classification

This paper explores the use of a learned classifier for post-OCR text correction. Experiments with the Arabic language show that this approach, which integrates a weighted confusion matrix and a shallow language model, improves the vast majority of segmentation and recognition errors, the most frequent types of error on our dataset.

Towards a More User-friendly Correction

We first present our view of detection and correction of syntactic errors. We then introduce a new correction method, based on heuristic criteria used to decide which correction should be preferred. Weighting of these criteria leads to a flexible and parametrable system, which can adapt itself to the user. A partitioning of the trees based on linguistic criteria: agreement rules, rather than computational criteria is then necessary. We end by proposing extensions to lexical correction and to some syntactic errors. Our aim is an adaptable and user-friendly system capable of automatic correction…

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction

Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models. Previous error correction methods usually take the source (incorrect) sentence as encoder input and generate the target (correct) sentence through the decoder. Since the error rate of the incorrect sentence is usually low (e.g., 10\%), the correction model can only learn to correct on limited error tokens but trivially copy on most tokens (correct tokens), which harms the effective training of error correction. In this paper, we argue that the correct toke…