Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Abstract
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability. In this paper, we aim to improve word embeddings by 1) incorporating more contextual information from existing pre-trained models into the Skip-gram framework, which we call Context-to-Vec; 2) proposing a post-processing retrofitting method for static embeddings independent of training by employing priori synonym knowledge and weighted vector distribution. Through extrinsic and intrinsic tasks, our methods are well proven to outperform the baselines by a large margin.
Cite
@article{arxiv.2210.16848,
title = {Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings},
author = {Jiangbin Zheng and Yile Wang and Ge Wang and Jun Xia and Yufei Huang and Guojiang Zhao and Yue Zhang and Stan Z. Li},
journal= {arXiv preprint arXiv:2210.16848},
year = {2023}
}
Comments
Accepted to ACL 2022