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Found 23 papers, 12 papers with code
Shared Task on Feedback Comment Generation for Language Learners
In this paper, we propose a generation challenge called Feedback comment generation for language learners.
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data.
FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation
In this study, we collaborate with in-house ad creators to refine the CAMERA references and develop an alternative ATG evaluation dataset called FaithCAMERA, in which the faithfulness of references is guaranteed.
AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising
With the increase in the more fluent ad texts automatically created by natural language generation technology, it is in the high demand to verify the quality of these creatives in a real-world setting.
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
Recent models for natural language understanding are inclined to exploit simple patterns in datasets, commonly known as shortcuts.
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation.
Revisiting Meta-evaluation for Grammatical Error Correction
The results of improved correlations by aligning the granularity in the sentence-level meta-evaluation, suggest that edit-based metrics may have been underestimated in existing studies.
Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG).
Japanese Lexical Complexity for Non-Native Readers: A New Dataset
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale.
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges.
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction
Therefore, in this study, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC.
Proficiency Matters Quality Estimation in Grammatical Error Correction
This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data.
Do Grammatical Error Correction Models Realize Grammatical Generalization?
There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data.
Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation
The performance measures are based on the simple idea that the more systems successfully correct an error, the easier it is considered to be.
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets.
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
We introduce a new task formulation of SAS that matches the actual usage.
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC.
Ranked #2 on
Grammatical Error Correction
on JFLEG
GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC).
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models.
Ranked #13 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
The AIP-Tohoku System at the BEA-2019 Shared Task
We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture.
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models.
