|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 显示文摘 | Xinli Jing Yangyong Wang Dan Wu Jipeng Qiang | 2007 | Ultrason Sonochem2007，14，： | 1 |
| 2 | Representation learning via an integrated autoencoder for unsupervised domain adaptation显示文摘The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target domain.The key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain discrepancy.Recently,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain adaptation.However,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain features.To address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called IAUDA.To capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature representations.After higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain representations.In addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local area.Experimental results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods. | Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI | 2023 | Frontiers of Computer Science2023，17，5： | 0 |
| 3 | Lexical simplification via single-word generation显示文摘1 Introduction Lexical simplification(LS)aims to simplify a sentence by replacing complex words with simpler words without changing the meaning of the sentence,which can facilitate comprehension of the text for people with non-native speakers and children.Traditional LS methods utilize linguistic databases(e.g.,WordNet)[1]or word embedding models[2]to extract synonyms or high-similar words for the complex word,and then sort them based on their appropriateness in context.Recently,BERT-based LS methods[3,4]entirely or partially mask the complex word of the original sentence,and then feed the sentence into pretrained modeling BERT[5]to obtain the top probability tokens corresponding to the masked word as the substitute candidates.They have made remarkable progress in generating substitutes by making full use of the context information of complex words,that can effectively alleviate the shortcomings of traditional methods. | Jipeng QIANG Yang LI Yun LI Yunhao YUAN Yi ZHU | 2023 | Frontiers of Computer Science2023，17，6： | 0 |
| 4 | Safeguarding text generation API’s intellectual property through meaning-preserving lexical watermarks显示文摘1 Introduction Recent advancements in encoder-decoder based text generation technology,like ChatGPT by OpenAI,and PaLM[1]by Google,have garnered attention in the AI community.Pay-per-use APIs offer access to these models,but research shows they are prone to imitation attacks,where malicious users train their models through skillfully crafted queries to get responses from lawful APIs.Such attacks violate the intellectual property(IP)and deter further research[2].Recent work introduced lexical watermarking(LW)methods to protect legal APIs’IP.LW modifies the original outputs and uses null-hypothesis test for ownership verification on imitation models[2,3].High-frequency words are selected,and WordNet synonyms replace them,but this one-size-fits-all approach neglects rational substitutes. | Shiyu ZHU Yun LI Xiaoye OUYANG Xiaocheng HU Jipeng QIANG | 2023 | Frontiers of Computer Science2023，17，6： | 0 |
| 5 | Unsupervised statistical text simplification using pre-trained language modeling for initialization显示文摘Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines. | Jipeng QIANG Feng ZHANG Yun LI Yunhao YUAN Yi ZHU Xindong WU | 2023 | Frontiers of Computer Science2023，17，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员