期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：3213-3221
DOI：10.18653/v1/2021.eacl-main.280
语种：English
出版社：ACL Anthology
摘要：Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models. However, it is hardly applicable in real-world datasets as word embedding is generally studied with a well-refined corpus. Notably, in Hangeul (Korean writing system), which has a unique writing system, various kinds of Out-Of-Vocabulary (OOV) appear from typos. In this paper, we propose a robust Hangeul word embedding model against typos, while maintaining high performance. The proposed model utilizes a Convolutional Neural Network (CNN) architecture with a channel attention mechanism that learns to infer the original word embeddings. The model train with a dataset that consists of a mix of typos and correct words. To demonstrate the effectiveness of the proposed model, we conduct three kinds of intrinsic and extrinsic tasks. While the existing embedding models fail to maintain stable performance as the noise level increases, the proposed model shows stable performance.
