Jiangbin Zheng


2022

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Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Jiangbin Zheng | Yile Wang | Ge Wang | Jun Xia | Yufei Huang | Guojiang Zhao | Yue Zhang | Stan Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.

2020

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A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Yiqi Tong | Jiangbin Zheng | Hongkang Zhu | Yidong Chen | Xiaodong Shi
Proceedings of the 28th International Conference on Computational Linguistics

Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.