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 ...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.展开更多
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,...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.展开更多
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 a...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.展开更多
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.
基金This work was partially supported by the National Natural Science Foundation of China(No.61876205)the Natural Science Foundation of Guangdong(No.2021A1515012652)the Science and Technology Program of Guangzhou(No.2019050001).
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.
基金supported by Natural Science Foundation of China(Nos.62006224 and 62122088).
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.
