Search Results for author: Mingwen Wang

Found 16 papers, 3 papers with code

融合XLM词语表示的神经机器译文自动评价方法(Neural Automatic Evaluation of Machine Translation Method Combined with XLM Word Representation)

no code implementations CCL 2021 Wei Hu, Maoxi Li, Bailian Qiu, Mingwen Wang

“机器译文自动评价对机器翻译的发展和应用起着重要的促进作用, 它一般通过计算机器译文和人工参考译文的相似度来度量机器译文的质量。该文通过跨语种预训练语言模型XLM将源语言句子、机器译文和人工参考译文映射到相同的语义空间, 结合分层注意力和内部注意力提取源语言句子与机器译文、机器译文与人工参考译文以及源语言句子与人工参考译文之间差异特征, 并将其融入到基于Bi-LSTM神经译文自动评价方法中。在WMT’19译文自动评价数据集上的实验结果表明, 融合XLM词语表示的神经机器译文自动评价方法显著提高了其与人工评价的相关性。”

Machine Translation

“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)

no code implementations CCL 2020 Bailian Qiu, Mingwen Wang, Maoxi Li, Cong Chen, Fan Xu

机器翻译错误分析旨在找出机器译文中存在的错误, 包括错误类型、错误分布等, 它在机器翻译研究和应用中起着重要作用。该文将人工译后编辑与错误分析结合起来, 对译后编辑操作进行错误标注, 采用自动标注和人工标注相结合的方法, 构建了一个细粒度英汉机器翻译错误分析语料库, 其中每一个标注样本包括源语言句子、机器译文、人工参考译文、译后编辑译文、词错误率和错误类型标注;标注的错误类型包括增词、漏词、错词、词序错误、未译和命名实体翻译错误等。标注的一致性检验表明了标注的有效性;对标注语料的统计分析结果能有效地指导机器翻译系统的开发和人工译员的后编辑。

Machine Translation

基于自动识别的委婉语历时性发展变化与社会共变研究(A Study on the Diachronic Development and Social Covariance of Euphemism Based on Automatic Recognition)

no code implementations CCL 2021 Chenlin Zhang, Mingwen Wang, Yiming Tan, Ming Yin, Xinyi Zhang

“本文主要以汉语委婉语作为研究对象, 基于大量人工标注, 借助机器学习有监督分类方法, 实现了较高精度的委婉语自动识别, 并基于此对1946年-2017年的《人民日报》中的委婉语历时变化发展情况进行量化统计分析。从大规模数据的角度探讨委婉语历时性发展变化、委婉语与社会之间的共变关系, 验证了语言的格雷什姆规律与更新规律。”

Finger Vein Recognition by Generating Code

no code implementations21 Jan 2021 Zhongxia Zhang, Mingwen Wang

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure.

Semantic Segmentation

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

no code implementations5 Jan 2021 Qiaosi Yi, Yunxing Liu, Aiwen Jiang, Juncheng Li, Kangfu Mei, Mingwen Wang

Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.

Crowd Counting Density Estimation

StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding

1 code implementation16 Dec 2020 Jinshan Zeng, Qi Chen, Yunxin Liu, Mingwen Wang, Yuan YAO

However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results.

Font Generation

Progressive Feature Fusion Network for Realistic Image Dehazing

1 code implementation4 Oct 2018 Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang

Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.

Image Dehazing Single Image Dehazing

An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks

1 code implementation3 Oct 2018 Kangfu Mei, Aiwen Jiang, Juncheng Li, Jihua Ye, Mingwen Wang

Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.

Image Super-Resolution

An entity-driven recursive neural network model for chinese discourse coherence modeling

no code implementations14 Apr 2017 Fan Xu, Shujing Du, Maoxi Li, Mingwen Wang

Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing field. Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated features to capture the logic or syntactic or semantic relationships acrosssentences within a text. In this paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation based on current English discourse coherenceneural network model.

Coherence Evaluation Feature Engineering +3

Sentence-level dialects identification in the greater China region

no code implementations8 Jan 2017 Fan Xu, Mingwen Wang, Maoxi Li

Identifying the different varieties of the same language is more challenging than unrelated languages identification.

Word Alignment

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