Pseudo-Error Generation for Grammatical Error Correction Based on Learner’s First Language
We propose to adapt grammatical error correction (GEC) systems to the learners' first language (L1) by generating artificial errors that reflect the L1 influence. To this end, we employ two simple approaches: fine-tuning a back-translation model on L1-annotated data; and controlling the output of a back-translation model and generating artificial errors that follow the L1-dependant error type distribution. We demonstrate that, despite the simplicity of the model and the paucity of the L1-annotated data, our methods succeed in adapting GEC models to some languages. We also show that generating L1-adapted artificial errors is orthogonal to the existing method that directly adapts the GEC model to each L1. Lastly, we present an analysis of the pseudo errors generated by our models and show that they approximately capture the L1-specific error patterns.
Code (0)
등록된 구현이 없습니다.
Tasks
Grammatical Error CorrectionTranslationSimilar Papers 제목 키워드 기반
Comparison of Grammatical Error Correction Using Back-Translation Models
Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Therefore, GEC studies have developed various methods to generate pseudo data, which comprise pairs of grammatical and artificially prod…
Grammatical Error CorrectionTranslationDo 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. However, these approaches suffer from several issues that make them inconvenient for real-world …
Grammatical Error CorrectionGrammatical Error Correction Using Pseudo Learner Corpus Considering Learner's Error Tendency
Recently, several studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. However, a large amount of pseudo data are required to train an accurate GEC model. To ad…
Grammatical Error CorrectionAn 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. However, consensus is lacking on experimental configur…
Grammatical Error Correction(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC syst…
Grammatical Error CorrectionMachine TranslationTranslation