Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples
A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one. However, the performance of GEC models with the seq2seq framework heavily relies on the size and quality of the corpus on hand. We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set. Extensive experimental results show that such adversarial training can improve both the generalization and robustness of GEC models.
Code (0)
등록된 구현이 없습니다.
Tasks
Grammatical Error CorrectionSentenceSimilar Papers 제목 키워드 기반
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting
Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-trai…
Adversarial AttackGrammatical Error CorrectionAdversarial Grammatical Error Correction
Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving st…
Grammatical Error CorrectionMachine TranslationNMTSentence+2Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-trai…
Adversarial AttackGrammatical Error CorrectionOrganic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs
Grammatical Error Correction has seen significant progress with the recent advancements in deep learning. As those methods require huge amounts of data, synthetic datasets are being built to fill this gap. Unfortunately,…
Grammatical Error CorrectionCombining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing. This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). CGED aims to diagnose four types of gramm…
Position