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2020 AACL AACL 2020

TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 2020

Abstract

AbstractIn this paper, we introduce our system for NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we treat the grammar error diagnosis (GED) task as a grammatical error correction (GEC) problem and propose a method that incorporates a pre-trained model into an encoder-decoder model to solve this problem.

🚀 Conference Pioneer — AACL 2020
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐣 Hot Topic Early Bird — text generation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Hongfei Wang , Mamoru Komachi

Topics

Deep Learning > Architectures > Transformers Natural Language Processing > Generation > Text Generation Machine Learning > Learning Types > Transfer Learning Natural Language Processing > Applications > Text Generation Deep Learning > Techniques > Transfer Learning

Keywords

transfer learning grammatical error correction text generation pre-trained model pre-trained language model encoder-decoder model chinese language
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