CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers

• URL: http://arxiv.org/abs/2211.08788v3
• Date: Thu, 23 May 2024 04:41:10 GMT
• Title: CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers
• Authors: Yong Hu, Fandong Meng, Jie Zhou,
• Abstract summary: We present CSCD-NS, the first Chinese spelling check dataset designed for native speakers. CSCD-NS is ten times larger in scale and exhibits a distinct error distribution. We propose a novel method that simulates the input process through an input method.
• Score: 62.61866477815883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement.

Related papers

• EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling Correction [0.0]
  Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in Chinese sentences caused by phonetic or visual similarities. We propose two data augmentation methods to address these limitations. Firstly, we augment the dataset by either splitting long sentences into shorter ones or reducing typos in sentences with multiple typos.
  arXiv  Detail & Related papers  (2024-09-08T14:29:10Z)
• C-LLM: Learn to Check Chinese Spelling Errors Character by Character [61.53865964535705]
  We propose C-LLM, a Large Language Model-based Chinese Spell Checking method that learns to check errors Character by Character. C-LLM achieves an average improvement of 10% over existing methods.
  arXiv  Detail & Related papers  (2024-06-24T11:16:31Z)
• Contextual Spelling Correction with Language Model for Low-resource Setting [0.0]
  A small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Probability of error happening(error model) is extracted from the corpus. Combination of LM and error model is used to develop the SC model through the well-known noisy channel framework.
  arXiv  Detail & Related papers  (2024-04-28T05:29:35Z)
• Make BERT-based Chinese Spelling Check Model Enhanced by Layerwise Attention and Gaussian Mixture Model [33.446533426654995]
  We design a heterogeneous knowledge-infused framework to strengthen BERT-based CSC models. We propose a novel form of n-gram-based layerwise self-attention to generate a multilayer representation. Experimental results show that our proposed framework yields a stable performance boost over four strong baseline models.
  arXiv  Detail & Related papers  (2023-12-27T16:11:07Z)
• Rethinking Masked Language Modeling for Chinese Spelling Correction [70.85829000570203]
  We study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model. We find that fine-tuning BERT tends to over-fit the error model while under-fit the language model, resulting in poor generalization to out-of-distribution error patterns. We demonstrate that a very simple strategy, randomly masking 20% non-error tokens from the input sequence during fine-tuning is sufficient for learning a much better language model without sacrificing the error model.
  arXiv  Detail & Related papers  (2023-05-28T13:19:12Z)
• Error-Robust Retrieval for Chinese Spelling Check [43.56073620728942]
  Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts. Previous methods may not fully leverage the existing datasets. We introduce our plug-and-play retrieval method with error-robust information for Chinese Spelling Check.
  arXiv  Detail & Related papers  (2022-11-15T01:55:34Z)
• Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition [52.55136323341319]
  Existing Chinese text error detection mainly focuses on spelling and simple grammatical errors. Chinese semantic errors are understudied and more complex that humans cannot easily recognize.
  arXiv  Detail & Related papers  (2022-04-15T13:55:32Z)
• Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding [76.89426311082927]
  Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference. We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space. Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
  arXiv  Detail & Related papers  (2021-04-13T17:54:33Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.