| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |
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NeuSpell: A Neural Spelling Correction Toolkit (2020.emnlp-demos)
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| Challenge: | a new spelling correction toolkit is available for free. |
| Approach: | They propose an open-source toolkit for spelling correction in English . they train neural models using spelling errors in context and using richer contextual representations. |
| Outcome: | The proposed spell-checker improves accuracy on synthetic examples and richer representations of the context. |
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (2020.lrec-1)
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| Challenge: | a recent study shows that typographical errors are now ubiquitous . traditional spelling correction software is inadequate to correct typographical mistakes . |
| Approach: | They propose to generate typographical errors based on annotated spelling errors . they then use annotations to introduce errors into substantially larger corpora . |
| Outcome: | The proposed method generates typographical errors that require context-aware error detection . it also shows that machine learning can correct typographical mistakes based on the data . |
Spelling Error Correction with Soft-Masked BERT (2020.acl-main)
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| Challenge: | Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT. |
| Approach: | They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique. |
| Outcome: | The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems. |
An Error-Guided Correction Model for Chinese Spelling Error Correction (2022.findings-emnlp)
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| Challenge: | Existing neural network approaches have achieved great progress on Chinese spelling correction, but there is still room for improvement. |
| Approach: | They propose an error-guided correction model that uses pre-trained BERT models to detect errors and integrate the error confusion set into the model. |
| Outcome: | The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves superior performance on both quality and computation speed. |
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning (P18-3)
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| Challenge: | Indic languages are resource-scarce and do not have such parallel data due to low volume of queries. |
| Approach: | They propose a sequence-to-sequence deep learning model which trains end-to end for Indic languages, Hindi and Telugu. |
| Outcome: | The proposed model is competitive with existing spell checking and correction techniques for Indic languages. |
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)
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| Challenge: | Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder. |
| Approach: | They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence. |
| Outcome: | The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models. |
CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction (2022.findings-acl)
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| Challenge: | Recent research on Chinese spelling correction methods has poor performance on multi-typo texts. |
| Approach: | They propose to use Bert-based Chinese spelling correction models to overcome these limitations by constructing a noisy context for each training sample and a copy mechanism to encourage the model to choose the input character when the miscorrected and input character are both valid. |
| Outcome: | The proposed model outperforms state-of-the-art models on widely used benchmarks and achieves a remarkable gain. |
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)
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| Challenge: | Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC) |
| Approach: | They propose a sequence-to-sequence learning framework with neural networks that generates more valuable training instances and adds task-specific examples to enhance the model. |
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A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)
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| Challenge: | Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes. |
| Approach: | They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task. |
| Outcome: | The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models. |
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction (N18-1)
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| Challenge: | Existing grammar correction methods are limited in their ability to correct highly local errors . evaluators are unable to distinguish noisy examples from nonsynthesized ones . |
| Approach: | They propose to synthesize parallel data by noising a clean monolingual corpus . they propose to apply noise to the corpus to syntherize additional noisy examples . |
| Outcome: | The proposed method can produce almost as strong results as training with nonsynthesized data. |