Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems

• URL: http://arxiv.org/abs/2508.06810v1
• Date: Sat, 09 Aug 2025 04:06:18 GMT
• Title: Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems
• Authors: Steven Coyne, Diana Galvan-Sosa, Ryan Spring, Camélia Guerraoui, Michael Zock, Keisuke Sakaguchi, Kentaro Inui,
• Abstract summary: We introduce an annotation framework that models each error's error type and generalizability.<n>We collect a dataset of annotated learner errors and corresponding human-written feedback comments.<n>We evaluate keyword-guided, keyword-free, and template-guided methods of generating feedback.
• Score: 25.047364950581265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in natural language processing (NLP) have contributed to the development of automated writing evaluation (AWE) systems that can correct grammatical errors. However, while these systems are effective at improving text, they are not optimally designed for language learning. They favor direct revisions, often with a click-to-fix functionality that can be applied without considering the reason for the correction. Meanwhile, depending on the error type, learners may benefit most from simple explanations and strategically indirect hints, especially on generalizable grammatical rules. To support the generation of such feedback, we introduce an annotation framework that models each error's error type and generalizability. For error type classification, we introduce a typology focused on inferring learners' knowledge gaps by connecting their errors to specific grammatical patterns. Following this framework, we collect a dataset of annotated learner errors and corresponding human-written feedback comments, each labeled as a direct correction or hint. With this data, we evaluate keyword-guided, keyword-free, and template-guided methods of generating feedback using large language models (LLMs). Human teachers examined each system's outputs, assessing them on grounds including relevance, factuality, and comprehensibility. We report on the development of the dataset and the comparative performance of the systems investigated.

Related papers

• Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models [57.758735361535486]
  TGEA is an error-annotated dataset for text generation from pretrained language models (PLMs)<n>We create an error taxonomy to cover 24 types of errors occurring in PLM-generated sentences.<n>This is the first dataset with comprehensive annotations for PLM-generated texts.
  arXiv  Detail & Related papers  (2025-03-06T09:14:02Z)
• DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers [18.279429202248632]
  We introduce DISCERN, a framework for interpreting systematic biases in text classifiers using language explanations. DISCERN iteratively generates precise natural language descriptions of systematic errors by employing an interactive loop between two large language models. We show that users can interpret systematic biases more effectively (by over 25% relative) and efficiently when described through language explanations as opposed to cluster exemplars.
  arXiv  Detail & Related papers  (2024-10-29T17:04:55Z)
• Neural Automated Writing Evaluation with Corrective Feedback [4.0230668961961085]
  We propose an integrated system for automated writing evaluation with corrective feedback. This system enables language learners to simulate the essay writing tests. It would also alleviate the burden of manually correcting innumerable essays.
  arXiv  Detail & Related papers  (2024-02-27T15:42:33Z)
• 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)
• You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting [24.738004421537926]
  Small language models like T5 excel in generating high-quality text for data-to-text tasks.<n>They frequently miss keywords, which is considered one of the most severe and common errors in this task.<n>We explore the potential of using feedback systems to enhance semantic fidelity in smaller language models for data-to-text generation tasks.
  arXiv  Detail & Related papers  (2023-06-28T05:34:25Z)
• Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors [105.12462629663757]
  In this work, we aggregate factuality error annotations from nine existing datasets and stratify them according to the underlying summarization model. We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models.
  arXiv  Detail & Related papers  (2022-05-25T15:26:48Z)
• 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)
• A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction [83.14159143179269]
  Grammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences. We propose a syntax-guided GEC model (SG-GEC) which adopts the graph attention mechanism to utilize the syntactic knowledge of dependency trees. We evaluate our model on public benchmarks of GEC task and it achieves competitive results.
  arXiv  Detail & Related papers  (2021-11-05T07:07:48Z)
• Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale [52.663117551150954]
  A few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models.
  arXiv  Detail & Related papers  (2020-10-26T13:57:20Z)
• Classifying Syntactic Errors in Learner Language [32.93997096837712]
  We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems.
  arXiv  Detail & Related papers  (2020-10-21T14:28:22Z)
• On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
  We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. Results confirm that the performance of all tested models is affected but the degree of impact varies.
  arXiv  Detail & Related papers  (2020-05-12T11:01:44Z)

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.