Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction

• URL: http://arxiv.org/abs/2501.00334v1
• Date: Tue, 31 Dec 2024 08:11:49 GMT
• Title: Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction
• Authors: Ding Zhang, Yangning Li, Lichen Bai, Hao Zhang, Yinghui Li, Haiye Lin, Hai-Tao Zheng, Xin Su, Zifei Shan,
• Abstract summary: Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Current approaches ignore that correction difficulty varies across different instances and treat these samples equally. We propose a multi-granularity Curriculum Learning framework to address this problem.
• Score: 21.82403446634522
• License:
• Abstract: Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to improve the performance. However, current approaches ignore that correction difficulty varies across different instances and treat these samples equally, enhancing the challenge of model learning. To address this problem, we propose a multi-granularity Curriculum Learning (CL) framework. Specifically, we first calculate the correction difficulty of these samples and feed them into the model from easy to hard batch by batch. Then Instance-Level CL is employed to help the model optimize in the appropriate direction automatically by regulating the loss function. Extensive experimental results and comprehensive analyses of various datasets prove the effectiveness of our method.

Related papers

• Subtle Errors Matter: Preference Learning via Error-injected Self-editing [59.405145971637204]
  We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE) RISE injects predefined subtle errors into partial tokens of correct solutions to construct hard pairs for error mitigation. Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH.
  arXiv  Detail & Related papers  (2024-10-09T07:43:38Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• 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)
• 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)
• On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning [71.44986275228747]
  In-context learning (ICL) has become an efficient approach propelled by the recent advancements in large language models (LLMs) However, both paradigms are prone to suffer from the critical problem of overconfidence (i.e., miscalibration)
  arXiv  Detail & Related papers  (2023-12-21T11:55:10Z)
• Understanding and Mitigating Classification Errors Through Interpretable Token Patterns [58.91023283103762]
  Characterizing errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors. We propose to discover those patterns of tokens that distinguish correct and erroneous predictions. We show that our method, Premise, performs well in practice.
  arXiv  Detail & Related papers  (2023-11-18T00:24:26Z)
• Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task [10.597024796304016]
  Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks. This report explores the how large language models perform on Chinese grammatical error correction tasks.
  arXiv  Detail & Related papers  (2023-07-08T13:10:59Z)
• 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)
• uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers [23.343006562849126]
  We propose a framework named textbfuChecker to conduct unsupervised spelling error detection and correction. Masked pretrained language models such as BERT are introduced as the backbone model. Benefiting from the various and flexible MASKing operations, we propose a Confusionset-guided masking strategy to fine-train the masked language model.
  arXiv  Detail & Related papers  (2022-09-15T05:57:12Z)
• Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models [51.744357472072416]
  We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances. Experimental results show that such an adversarial training method combined with the pretraining strategy can improve both the generalization and robustness of multiple CSC models.
  arXiv  Detail & Related papers  (2021-05-31T09:17:33Z)

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.