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4th International Conference on Artificial intelligence and Electromechanical Automation, AIEA 2023

作者： Guo, Zicheng Rao, Yuan Xi'an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China

ISBN: (纸本)9781510666412

Text style transfer is mainly to modify the text style to suit various application scenarios without changing the semantic meaning of the text, which is a great significant issue in natural language processing. To expedite research progress, this survey conducts a systematic review of existing literature. Drawing from a vast body of research, this survey first extracts the essential connotations of style information in text of varying granularity across different tasks, and then provide a clear definition of text style transfer, summarize the main challenges at present and comprehensively codify and discuss the current datasets used for evaluation, as well as their indicators. Moreover, this survey compares the mechanisms, advantages, and shortcomings of unsupervised classical methods across word, sentence, and paragraph levels. Finally, the future directions of fighting are also given, hoping to facilitate more comprehensive solutions. © 2023 SPIE.

关键词： Semantics

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Improving Knowledge Graph Completion Using Soft Rules and Adversarial Learning

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Chinese Journal of Electronics 2021年第4期30卷 623-633页

作者： TANG Caifang RAO Yuan YU Hualei SUN Ling CHENG Jiamin WANG Yutian Xi'an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software EngineeringXi'an Jiaotong University Shaanxi Joint Key Laboratory for Artifact Intelligence School of Software EngineeringXi'an Jiaotong University School of Software Engineering Xi'an Jiaotong University

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks. However, the existing knowledge graph are incomplete. Therefore, knowledge graph completion technology has become a research hotspot in the field of artificial intelligence, but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge *** on the logic rules of knowledge and the role of adversarial learning in knowledge embedding, we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning(RUGA). Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method, and high-quality negative samples are obtained. Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model. The whole model will benefit from both high-quality samples and logical rules. In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively. Finally, the experimental results show that the model can effectively improve the effect of knowledge graph completion.

关键词： Knowledge graph Knowledge embedding Knowledge graph completion Adversarial learning

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Graph Explicit Attention Network Based on Predefined Strategy 12

Graph Explicit Attention Network Based on Predefined Strateg...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Zhang, Xiangbo Rao, Yuan Sun, Ling Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

Graph attention network (GAT) has achieved great success in graph representation learning in recent years. However, the lower Physical properties of GAT in training process severely affects the application of attention mechanism in graph domain. Particularly, the attention coefficients and the multi-head mechanism are calculated or introduced in the process of model training, which obviously increases the storage complexity. In this paper, we present a graph explicit attention network (GEAT), a novel graph attention architecture that leverage predefined strategy to calculate attention coefficients, which combine global structural and node feature information. In this way, GEAT can effectively reduce the storage complexity, improve the training efficiency and alleviate the over-smoothing problem of attention scores. Experiments on benchmark datasets — Core, Citeseer and Pubmed — demonstrate that our model outperforms the state-of-the-art methods with a clear margin in challenging classifcation tasks, while being computationally efficient. © 2022 The Authors. Published by Elsevier B.V.

关键词： Attention mechanism Deep Learning Graph neural network

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Transformer-based Hierarchical Topic-to-Essay Generation 12

Transformer-based Hierarchical Topic-to-Essay Generation

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： He, Wangbo Rao, Yuan Xi'an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Shaanxi Joint Key Laboratory for Artifact Intelligence School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China

With the development of 5G network and Internet of things (IOT), a large amount of information is required. In this work, we focus on Topic-to-Essay Generation (TEG), which aims to generate the text based on the topics. Existing methods utilize the RNN-based models, and it's not so useful to capture the long dependencies in the text. The information of topics is insufficient to generate a long text and the existing methods also suffer from the problem about the topic relevance of the text. To fill these gaps, we propose a Transformer-based Hierarchical Topic-to-Essay Generation Model (THTEG), and the experimental results on a real dataset show that our model performs better than the baselines in terms of automatic evaluation and human evaluation. © 2022 The Authors. Published by Elsevier B.V.

关键词： Essay generation Text generation Transformer

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Graph Explicit Attention Network Based on Predefined Strategy

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Procedia Computer Science 2022年 202卷 422-429页

作者： Xiangbo Zhang Yuan Rao Ling Sun Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi 710049 China Shannxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi’an Jiaotong University) China Research Institute of Xi’an Jiaotong University Shenzhen China

关键词： Deep Learning Graph neural network Attention mechanism

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Transformer-based Hierarchical Topic-to-Essay Generation

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Procedia Computer Science 2022年 202卷 414-421页

作者： Wangbo He Yuan Rao Xi’an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi 710049 China Shaanxi Joint Key Laboratory for Artifact Intelligence School of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi 710049 China

With the development of 5G network and Internet of things (IOT), a large amount of information is required. In this work, we focus on Topic-to-Essay Generation (TEG), which aims to generate the text based on the topics. Existing methods utilize the RNN-based models, and it’s not so useful to capture the long dependencies in the text. The information of topics is insufficient to generate a long text and the existing methods also suffer from the problem about the topic relevance of the text. To fill these gaps, we propose a Transformer-based Hierarchical Topic-to-Essay Generation Model (THTEG), and the experimental results on a real dataset show that our model performs better than the baselines in terms of automatic evaluation and human evaluation.

关键词： Essay generation text generation Transformer

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A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on social Media

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Chinese Journal of Electronics 2019年第4期28卷 754-763页

作者： WU Lianwei RAO Yuan YU Hualei WANG Yiming AMBREEN Nazir Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi'an Jiaotong University

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages(a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social *** specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

关键词： Information credibility evaluation Rumor detection social media Text classification

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Evidence-aware hierarchical interactive attention networks for explainable claim verification 29

Evidence-aware hierarchical interactive attention networks f...

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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Wu, Lianwei Rao, Yuan Yang, Xiong Wang, Wanzhen Nazir, Ambreen Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

ISBN: (纸本)9780999241165

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Unified Dual-view Cognitive Model for Interpretable Claim Verification

arXiv

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arXiv 2021年

作者： Wu, Lianwei Rao, Yuan Lan, Yuqian Sun, Ling Qi, Zhaoyin Xi’an Key Lab. of Social Intelligence and Complexity Data Processing School of Software Engineering Xi’an Jiaotong University China China Research Institute of Xi’an Jiaotong University Shenzhen China

Recent studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users, the captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased information. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. For collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users to generate global evidence. For individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise an inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm the effectiveness of CICD. © 2021, CC BY.

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DTCA: Decision tree-based co-Attention networks for explainable claim verification

arXiv

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arXiv 2020年

作者： Wu, Lianwei Rao, Yuan Zhao, Yongqiang Liang, Hao Nazir, Ambreen Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University China China Research Institute of Xi'an Jiaotong University Shenzhen China

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-Attention Self-Attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence;and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-The-Art performance, boosting the F1-score by 3.11%, 2.41%, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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