Huizhen Wang


2021

pdf bib
利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)
Gechao Wang (王屹超) | Muhua Zhu (朱慕华) | Chen Xu (许晨) | Yan Zhang (张琰) | Huizhen Wang (王会珍) | Jingbo Zhu (朱靖波)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

视觉问答作为多模态任务,需要深度理解图像和文本问题从而推理出答案。然而在许多情况下,仅在图像和问题上进行简单推理难以得到正确的答案,事实上还有其它有效的信息可以被利用,例如图像描述、外部知识等。针对以上问题,本文提出了利用图像描述和外部知识增强表示的视觉问答模型。该模型以问题为导向,基于协同注意力机制分别在图像和其描述上进行编码,并且利用知识图谱嵌入,将外部知识编码到模型当中,丰富了模型的特征表示,增强模型的推理能力。在OKVQA数据集上的实验结果表明本文方法相比基线系统有1.71%的准确率提升,与先前工作中的主流模型相比也有1.88%的准确率提升,证明了本文方法的有效性。

2020

pdf bib
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
Yanyang Li | Yingfeng Luo | Ye Lin | Quan Du | Huizhen Wang | Shujian Huang | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64 55.53% between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.

pdf bib
Shallow-to-Deep Training for Neural Machine Translation
Bei Li | Ziyang Wang | Hui Liu | Yufan Jiang | Quan Du | Tong Xiao | Huizhen Wang | Jingbo Zhu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT is an open question. In this paper, we investigate the behavior of a well-tuned deep Transformer system. We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly. This inspires us to develop a shallow-to-deep training method that learns deep models by stacking shallow models. In this way, we successfully train a Transformer system with a 54-layer encoder. Experimental results on WMT’16 English-German and WMT’14 English-French translation tasks show that it is 1:4 faster than training from scratch, and achieves a BLEU score of 30:33 and 43:29 on two tasks. The code is publicly available at https://github.com/libeineu/SDT-Training.

2012

pdf bib
Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing
Muhua Zhu | Jingbo Zhu | Huizhen Wang
Proceedings of COLING 2012

2010

pdf bib
Boosting-Based System Combination for Machine Translation
Tong Xiao | Jingbo Zhu | Muhua Zhu | Huizhen Wang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
A Multi-stage Clustering Framework for Chinese Personal Name Disambiguation
Huizhen Wang | Haibo Ding | Yingchao Shi | Ji Ma | Xiao Zhou | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

2009

pdf bib
Chinese-English Organization Name Translation Based on Correlative Expansion
Feiliang Ren | Muhua Zhu | Huizhen Wang | Jingbo Zhu
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

pdf bib
Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation
Jingbo Zhu | Huizhen Wang | Eduard Hovy
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf bib
Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification
Jingbo Zhu | Huizhen Wang | Tianshun Yao | Benjamin K Tsou
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf bib
Learning a Stopping Criterion for Active Learning for Word Sense Disambiguation and Text Classification
Jingbo Zhu | Huizhen Wang | Eduard Hovy
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2006

pdf bib
Designing Special Post-Processing Rules for SVM-Based Chinese Word Segmentation
Muhua Zhu | Yilin Wang | Zhenxing Wang | Huizhen Wang | Jingbo Zhu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing