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arXiv 2022年

作者： Tu, Rong-Cheng Mao, Xian-Ling Lin, Kevin Qinghong Cai, Chengfei Qin, Weize Wang, Hongfa Wei, Wei Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China Beijing Institute of Technology Southeast Academy of Information Technology Fujian China National University of Singapore Singapore School of Computer Science Zhejiang University Zhejiang China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science Huazhong University of Science and Technology Wuhan China

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task. The source codes are available 1 Copyright © 2022, The Authors. All rights reserved.

关键词： Image retrieval

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Concept pointer network for abstractive summarization 9

Concept pointer network for abstractive summarization

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2019 Conference on Empirical Methods in Natural language processing and 9th International Joint Conference on Natural language processing, EMNLP-IJCNLP 2019

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9781950737901

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework. © 2019 Association for Computational Linguistics

关键词： Semantics

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A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation

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Tsinghua Science and Technology 2018年第6期23卷 660-670页

作者： Lerong Ma Lejian Liao DANDan Song Jingang Wang Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University

Knowledge Bases （KBs） are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation （CCR） can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporat features of entity-document instances, which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem, we present a Mixture of Experts （ME） model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.

关键词： knowledge base acceleration cumulative citation recommendation Mixture of Experts （ME） LatentEntity-Document Classes （LEDCs）

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A parallel recurrent neural network for language modeling with POS tags 31

A parallel recurrent neural network for language modeling wi...

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31st Pacific Asia Conference on language, information and Computation, PACLIC 2017

作者： Su, Chao Huang, Heyan Shi, Shumin Guo, Yuhang Wu, Hao School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing100081 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China

language models have been used in many natural language processing applications. In recent years, the recurrent neural network based language models have defeated the conventional n-gram based techniques. However, it is difficult for neural network architectures to use linguistic annotations. We try to incorporate part-of-speech features in recurrent neural network language model, and use them to predict the next word. Specifically, we proposed a parallel structure which contains two recurrent neural networks, one for word sequence modeling and another for part-of-speech sequence modeling. The state of part-of-speech network helped improve the word sequence's prediction. Experiments show that the proposed method performs better than the traditional recurrent network on perplexity and is better at reranking machine translation outputs. Copyright © 2017 Chao Su, Heyan Huang, Shumin Shi, Yuhang Guo and Hao Wu

关键词： Recurrent neural networks

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Exploring explainable selection to control abstract generation

arXiv

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arXiv 2020年

作者： Haonan, Wang Yang, Gao Yu, Bai Lapata, Mirella Heyan, Huang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute for Language Cognition and Computation School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

It is a big challenge to model long-range input for document summarization. In this paper, we target using a select and generate paradigm to enhance the capability of selecting explainable contents (i.e., interpret the selection given its semantics, novelty, relevance) and then guiding to control the abstract generation. Specifically, a new designed pair-wise extractor is proposed to capture the sentence pair interactions and its centrality. Furthermore, the generator is hybrid with the selected content and is jointly integrated with a pointer distribution that is derived from a sentence deployment's attention. The abstract generation can be controlled by an explainable mask matrix that determines to what extent the content can be included in the summary. Encoders are adaptable with both Transformerbased and BERT-based configurations. Overall, both results based on ROUGE metrics and human evaluation gain outperformance over several state-of-The-Art models on two benchmark CNN/DailyMail and NYT datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Abstracting

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Group Daily Arrival Prediction Based on A Hybrid Model Combing Autoregressive Integrated Moving Average Model and Back Propagation Neural Network

Group Daily Arrival Prediction Based on A Hybrid Model Combi...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Xiaoling Zhu Xi Zhang Ge Shi Yashen Wang National Engineering Laboratory for Risk Perception and Prevention (RPP) China Academy of Electronics and Information Technology of CETC Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Beijing Institute of Technology Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

To solve the problem of predicting daily arrival number of people belonging to a specific group, this paper proposes a hybrid model which integrates linear component (from ARIMA model) with non-linear component (from BP neural network). The experimental results indicate that, hybrid methodology that has both linear and nonlinear modeling capabilities is a good strategy for practical use.

关键词： Predictive models Neurons Data models Analytical models Urban areas Solid modeling Backpropagation

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Improving Parallel Corpus Quality for Chinese-Vietnamese Statistical Machine Translation

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Journal of Beijing Institute of Technology 2018年第1期27卷 127-136页

作者： Huu-anh Tran Yuhang Guo Ping Jian Shumin Shi Heyan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Application Beijing Institute of Technology

The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a very difficult task especially for low resource languages such as Chinese-Vietnamese. Fortunately,multilingual user generated contents（ UGC）,such as bilingual movie subtitles,provide us access to automatic construction of the parallel corpus. Although the amount of UGC parallel corpora can be considerable,the original corpus is not suitable for statistical machine translation（ SMT） systems. The corpus may contain translation errors,sentence mismatching,free translations,etc. To improve the quality of the bilingual corpus for SMT systems,three filtering methods are proposed： sentence length difference,the semantic of sentence pairs,and machine learning. Experiments are conducted on the Chinese to Vietnamese translation *** results demonstrate that all the three methods effectively improve the corpus quality,and the machine translation performance（ BLEU score） can be improved by 1. 32.

关键词： parallel corpus filtering low resource languages bilingual movie subtitles machine translation Chinese-Vietnamese translation

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Neural Chinese word segmentation as sequence to sequence translation

arXiv

引用

arXiv 2019年

作者： Shi, Xuewen Huang, Heyan Jian, Ping Guo, Yuhang Wei, Xiaochi Tang, Yikun Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct models based on local features rather than considering global information of input sequence. In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework. The model captures the global information from the input and directly outputs the segmented sequence. It can also tackle other NLP tasks with CWS jointly in an end-to-end mode. Experiments on Weibo, PKU and MSRA benchmark datasets show that our approach has achieved competitive performances compared with state-of-the-art methods. Meanwhile, we successfully applied our proposed model to jointly learning CWS and Chinese spelling correction, which demonstrates its applicability of multi-task fusion. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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Concept pointer network for abstractive summarization

arXiv

引用

arXiv 2019年

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this *** Codes 68U15, 68T50 Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge based systems

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Tag recommendation by word-level tag sequence modeling

arXiv

引用

arXiv 2019年

作者： Shi, Xuewen Huang, Heyan Zhao, Shuyang Jian, Ping Tang, Yi-Kun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

Recently, neural network based tag recommendation has gained much attention. Most of these methods regard tag recommendation as multi-label classification. They treat different tags as individual categories with fixed labels, without considering the rich relations among tags. Moreover, using a fixed number of categories cannot tackle dynamic, changing tags with ongoing topics. In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. For efficiently modeling the semantic dependencies among tags in tag sequence and the strong sequential relations among the tag-words, we propose an essential sequence-to-sequence model, named LSTM-Attention. The model inherits the advantages of recurrent network based encoder for sequential modeling and attention based decoder for learning relations globally. In addition, as a text generation method, the proposed model is able to generate unseen tags, which is more applicable and flexible to real scenarios. Extensive experimental results on two datasets, i.e., Zhihu and Weibo, clearly illustrate the proposed model significantly outperforms other state-of-the-art text classification based methods and well demonstrate its advantage of handling unseen tags. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

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