|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Neural machine translation:Challenges,progress and future显示文摘Machine translation(MT)is a technique that leverages computers to translate human languages automatically.Nowadays,neural machine translation(NMT)which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT.This article makes a review of NMT framework,discusses the challenges in NMT,introduces some exciting recent progresses and finally looks forward to some potential future research trends. | ZHANG JiaJun ZONG ChengQing | 2020 | Science China(Technological Sciences)2020，63，10： | 10 |
| 2 | Toward practical spoken language translation 显示文摘 | Zong Chengqing Seligman M | 2005 | Machine Translation2005，19，2： | 1 |
| 3 | An approach to automatic ac- quisition of translation templates based on phrase structure extrac- tion and alignment显示文摘 | Flu Ribs Zong Chengqing Xu Bo | 2006 | IEEE Transactions on Audio Speech and I~anguage Processing2006，14，5： | 1 |
| 4 | A Joint Model to Simulta-neously Identify and Align Bilingual Named Entities显示文摘 | Chen Yufeng Zong Chengqing Su Keh-Yih | 2013 | ComputationalLinguistics2013，39，2： | 1 |
| 5 | A joint model to sim- uhaneously identify and align bilingual named entities显示文摘 | Chen Yufeng Zong Chengqing Su Keh-Yih | 2013 | Compu- tational Linguistics2013，39，2： | 1 |
| 6 | Integrating generative and discriminative character-based models for Chinese word segmentation 显示文摘 | KUN Wang ZONG Chengqing SU Keh Yih | 2012 | ACM Transactions on Asian Language Information Processing2012，11，2： | 1 |
| 7 | Study on the Sustainable Development of the Tobacco-growing Area in South Anhui显示文摘Based on the actual situation of tobacco production in South Anhui tobacco-growing area,the paper analyzes several major constraints,and discusses several aspects such as tobacco production human resources,production of large-scale cultivation,science and technology service providers,the standardized production management and production security system. The countermeasures and suggestions for sustainable development are also put forward to provide a reference for the sustainable development of tobacco-growing area in South Anhui. | Chun JIANG Jingjing TIAN Chengqing ZONG | 2015 | Asian Agricultural Research2015，7，1： | 0 |
| 8 | Transformer: A General Framework from Machine Translation to Others显示文摘Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another.Recently,Transformer-based neural machine translation(NMT)has achieved great break-throughs and has become a new mainstream method in both methodology and applications.In this article,we conduct an overview of Transformer-based NMT and its extension to other tasks.Specifically,we first introduce the framework of Transformer,discuss the main challenges in NMT and list the representative methods for each challenge.Then,the public resources and toolkits in NMT are listed.Meanwhile,the extensions of Transformer in other tasks,including the other natural language processing tasks,computer vision tasks,audio tasks and multi-modal tasks,are briefly presented.Finally,possible future research directions are suggested. | Yang Zhao Jiajun Zhang Chengqing Zong | 2023 | Machine Intelligence Research2023，20，4： | 0 |
| 9 | CNN-Based Broad Learning for Cross-Domain Emotion Classification显示文摘Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy,an emotion classifier trained on source domain may not work well on target domain.Many researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion classification.However,the problem of emotion classification for cross-domain is rarely involved.In this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad learning.We first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad learning.Then,to take advantage of these two classifiers,we designed a co-training model to boost together for them.Finally,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method.The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods. | Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou | 2023 | Tsinghua Science and Technology2023，28，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员