Proceedings of the 31st ACM International Conference on Information & Knowledge Management最新文献_第7页

Faithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer 基于事实感知的一致性约束转换器的忠实抽象摘要

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557319

Yuanjie Lyu, Chen Zhu, Tong Xu, Zikai Yin, Enhong Chen

{"title":"Faithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer","authors":"Yuanjie Lyu, Chen Zhu, Tong Xu, Zikai Yin, Enhong Chen","doi":"10.1145/3511808.3557319","DOIUrl":"https://doi.org/10.1145/3511808.3557319","url":null,"abstract":"Abstractive summarization is a classic task in Natural Language Generation (NLG), which aims to produce a concise summary of the original document. Recently, great efforts have been made on sequence-to-sequence neural networks to generate abstractive sum- maries with a high level of fluency. However, prior arts mainly focus on the optimization of token-level likelihood, while the rich semantic information in documents has been largely ignored. In this way, the summarization results could be vulnerable to hallucinations, i.e., the semantic-level inconsistency between a summary and corresponding original document. To deal with this challenge, in this paper, we propose a novel fact-aware abstractive summarization model, named Entity-Relation Pointer Generator Network (ERPGN). Specially, we attempt to formalize the facts in original document as a factual knowledge graph, and then generate the high-quality summary via directly modeling consistency between summary and the factual knowledge graph. To that end, we first leverage two pointer net- work structures to capture the fact in original documents. Then, to enhance the traditional token-level likelihood loss, we design two extra semantic-level losses to measure the disagreement between a summary and facts from its original document. Extensive experi- ments on public datasets demonstrate that our ERPGN framework could outperform both classic abstractive summarization models and the state-of-the-art fact-aware baseline methods, with significant improvement in terms of faithfulness.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

User Recommendation in Social Metaverse with VR 基于VR的社交虚拟世界中的用户推荐

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557487

Bing Chen, De-Nian Yang

{"title":"User Recommendation in Social Metaverse with VR","authors":"Bing Chen, De-Nian Yang","doi":"10.1145/3511808.3557487","DOIUrl":"https://doi.org/10.1145/3511808.3557487","url":null,"abstract":"Social metaverse with VR has been viewed as a paradigm shift for social media. However, most traditional VR social platforms ignore emerging characteristics in a metaverse, thereby failing to boost user satisfaction. In this paper, we explore a scenario of socializing in metaverse with VR, which brings major advantages over conventional social media: 1) leverage flexible display of users' 360-degree viewports to satisfy individual user interests, 2) ensure the user feelings of co-existence, 3) prevent view obstruction to help users find friends in crowds, and 4) support socializing with digital twins. Therefore, we formulate the Co-presence, and Occlusion-aware Metaverse User Recommendation (COMUR) problem to recommend a set of rendered players for users in social metaverse with VR. We prove COMUR is an NP-hard optimization problem and design a dual-module deep graph learning framework (COMURNet) to recommend appropriate users for viewport display. Experimental results on real social metaverse datasets and a user study with Occulus Quest 2 manifest that the proposed model outperforms baseline approaches by at least 36.7% of solution quality.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Legal Charge Prediction via Bilinear Attention Network 基于双线性注意力网络的法律费用预测

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557379

Yuquan Le, Yuming Zhao, Meng Chen, Zhe Quan, Xiaodong He, KenLi Li

{"title":"Legal Charge Prediction via Bilinear Attention Network","authors":"Yuquan Le, Yuming Zhao, Meng Chen, Zhe Quan, Xiaodong He, KenLi Li","doi":"10.1145/3511808.3557379","DOIUrl":"https://doi.org/10.1145/3511808.3557379","url":null,"abstract":"The legal charge prediction task aims to judge appropriate charges according to the given fact description in cases. Most existing methods formulate it as a multi-class text classification problem and have achieved tremendous progress. However, the performance on low-frequency charges is still unsatisfactory. Previous studies indicate leveraging the charge label information can facilitate this task, but the approaches to utilizing the label information are not fully explored. In this paper, inspired by the vision-language information fusion techniques in the multi-modal field, we propose a novel model (denoted as LeapBank) by fusing the representations of text and labels to enhance the legal charge prediction task. Specifically, we devise a representation fusion block based on the bilinear attention network to interact the labels and text tokens seamlessly. Extensive experiments are conducted on three real-world datasets to compare our proposed method with state-of-the-art models. Experimental results show that LeapBank obtains up to 8.5% Macro-F1 improvements on the low-frequency charges, demonstrating our model's superiority and competitiveness.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130416459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Platform for Argumentative Zoning Annotation and Scientific Summarization 论证性区划标注与科学总结平台

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557193

Alaa El-Ebshihy, Annisa Maulida Ningtyas, Linda Andersson, Florina Piroi, A. Rauber

引用次数: 1

Nonlinear Causal Discovery in Time Series 时间序列中的非线性因果发现

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557660

Tianhao Wu, Xingyu Wu, Xin Wang, Shikang Liu, Huanhuan Chen

{"title":"Nonlinear Causal Discovery in Time Series","authors":"Tianhao Wu, Xingyu Wu, Xin Wang, Shikang Liu, Huanhuan Chen","doi":"10.1145/3511808.3557660","DOIUrl":"https://doi.org/10.1145/3511808.3557660","url":null,"abstract":"Recent years have witnessed the proliferation of the Functional Causal Model (FCM) for causal learning due to its intuitive representation and accurate learning results. However, existing FCM-based algorithms suffer from the ubiquitous nonlinear relations in time-series data, mainly because these algorithms either assume linear relationships, or nonlinear relationships with additive noise, or do not introduce additional assumptions but can only identify nonlinear causality between two variables. This paper contributes in particular to a practical FCM-based causal learning approach, which can maintain effectiveness for real-world nonstationary data with general nonlinear relationships and unlimited variable scale.Specifically, the non-stationarity of time series data is first exploited with the nonlinear independent component analysis, to discover the underlying components or latent disturbances. Then, the conditional independence between variables and these components is studied to obtain a relation matrix, which guides the algorithm to recover the underlying causal graph. The correctness of the proposal is theoretically proved, and extensive experiments further verify its effectiveness. To the best of our knowledge, the proposal is the first so far that can fully identify causal relationships under general nonlinear conditions.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128992603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Generative-Free Urban Flow Imputation 无生成城市流量估算

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557334

Senzhang Wang, Jiyue Li, Hao Miao, Junbo Zhang, Junxing Zhu, Jianxin Wang

{"title":"Generative-Free Urban Flow Imputation","authors":"Senzhang Wang, Jiyue Li, Hao Miao, Junbo Zhang, Junxing Zhu, Jianxin Wang","doi":"10.1145/3511808.3557334","DOIUrl":"https://doi.org/10.1145/3511808.3557334","url":null,"abstract":"Urban flow imputation, which aims to infer the missing flows of some locations based on the available flows of surrounding areas, is critically important to various smart city related applications such as urban planning and public safety. Although many methods are proposed to impute time series data, they may not be feasible to be directly applied on urban flow data due to the following reasons. First, urban flows have the complex spatial and temporal correlations which are much harder to be captured compared with time series data. Second, the urban flow data can be random missing (i.e., missing randomly in terms of times and locations) or block missing (i.e., missing for all locations in a particular time slot). Thus it is difficult for existing methods to work well on both scenarios. In this paper, we for the first time study the urban flow imputation problem and propose a generative-free Attention-based Spatial-Temporal Combine and Mix Completion Network model (AST-CMCN for short) to effectively address it. Specifically, AST-CMCN consists of a Spatial and Temporal Completion Network (SATCNet for short) and a Spatial-Temporal Mix Completion Network (STMCNet for short). SATCNet is composed of stacked GRUAtt modules to capture the geographical and temporal correlations of the urban flows, separately. STMCNet is designed to capture the complex spatial-temporal associations jointly between historical urban flows and current data. A Message Passing module is also proposed to capture new spatial-temporal patterns that never appear in the historical data. Extensive experiments on two large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Best Practices for Top-N Recommendation Evaluation: Candidate Set Sampling and Statistical Inference Techniques Top-N推荐评估的最佳实践:候选集抽样和统计推断技术

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557816

Ngozi Ihemelandu

引用次数: 0

Contrastive Knowledge Graph Error Detection 对比知识图错误检测

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557264

Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, Linchuan Xu

{"title":"Contrastive Knowledge Graph Error Detection","authors":"Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, Linchuan Xu","doi":"10.1145/3511808.3557264","DOIUrl":"https://doi.org/10.1145/3511808.3557264","url":null,"abstract":"Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are unknown and diverse, while ground-truth labels are rare or even unavailable. A traditional solution is to construct logical rules to verify triples, but it is not generalizable since different KGs have distinct rules with domain knowledge involved. Recent studies focus on designing tailored detectors or ranking triples based on KG embedding loss. However, they all rely on negative samples for training, which are generated by randomly replacing the head or tail entity of existing triples. Such a negative sampling strategy is not enough for prototyping practical KG errors, e.g., (Bruce_Lee, place_of_birth, China), in which the three elements are often relevant, although mismatched. We desire a more effective unsupervised learning mechanism tailored for KG error detection. To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED). It introduces contrastive learning into KG learning and provides a novel way of modeling KG. Instead of following the traditional setting, i.e., considering entities as nodes and relations as semantic edges, CAGED augments a KG into different hyper-views, by regarding each relational triple as a node. After joint training with KG embedding and contrastive learning loss, CAGED assesses the trustworthiness of each triple based on two learning signals, i.e., the consistency of triple representations across multi-views and the self-consistency within the triple. Extensive experiments on three real-world KGs show that CAGED outperforms state-of-the-art methods in KG error detection. Our codes and datasets are available at https://github.com/Qing145/CAGED.git.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121530431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Deep Extreme Mixture Model for Time Series Forecasting 时间序列预测的深度极值混合模型

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557282

Abilasha S, Sahely Bhadra, Ahmed Zaheer Dadarkar, P Deepak

{"title":"Deep Extreme Mixture Model for Time Series Forecasting","authors":"Abilasha S, Sahely Bhadra, Ahmed Zaheer Dadarkar, P Deepak","doi":"10.1145/3511808.3557282","DOIUrl":"https://doi.org/10.1145/3511808.3557282","url":null,"abstract":"Time Series Forecasting (TSF) has been a topic of extensive research, which has many real world applications such as weather prediction, stock market value prediction, traffic control etc. Many machine learning models have been developed to address TSF, yet, predicting extreme values remains a challenge to be effectively addressed. Extreme events occur rarely, but tend to cause a huge impact, which makes extreme event prediction important. Assuming light tailed distributions, such as Gaussian distribution, on time series data does not do justice to the modeling of extreme points. To tackle this issue, we develop a novel approach towards improving attention to extreme event prediction. Within our work, we model time series data distribution, as a mixture of Gaussian distribution and Generalized Pareto distribution (GPD). In particular, we develop a novel Deep eXtreme Mixture Model (DXtreMM) for univariate time series forecasting, which addresses extreme events in time series. The model consists of two modules: 1) Variational Disentangled Auto-encoder (VD-AE) based classifier and 2) Multi Layer Perceptron (MLP) based forecaster units combined with Generalized Pareto Distribution (GPD) estimators for lower and upper extreme values separately. VD-AE Classifier model predicts the possibility of occurrence of an extreme event given a time segment, and forecaster module predicts the exact value. Through extensive set of experiments on real-world datasets we have shown that our model performs well for extreme events and is comparable with the existing baseline methods for normal time step forecasting.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122549208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557709

Jieyu Yang, Zhaoxin Huan, Yong He, Ke Ding, Liang Zhang, Xiaolu Zhang, Jun Zhou, Linjian Mo

{"title":"Task Similarity Aware Meta Learning for Cold-Start Recommendation","authors":"Jieyu Yang, Zhaoxin Huan, Yong He, Ke Ding, Liang Zhang, Xiaolu Zhang, Jun Zhou, Linjian Mo","doi":"10.1145/3511808.3557709","DOIUrl":"https://doi.org/10.1145/3511808.3557709","url":null,"abstract":"In recommender systems, content-based methods and meta-learning involved methods usually have been adopted to alleviate the item cold-start problem. The former consider utilizing item attributes at the feature level and the latter aim at learning a globally shared initialization for all tasks to achieve fast adaptation with limited data at the task level. However, content-based methods only focus on the similarity of item attributes, ignoring the relationships established by user interactions. And for tasks with different distributions, most meta-learning-based methods are difficult to achieve better performance under a single initialization. To address the limitations mentioned above and combine the strengths of both methods, we propose a Task Similarity Aware Meta-Learning (TSAML) framework from two aspects. Specifically, at the feature level, we simultaneously introduce content information and user-item relationships to exploit task similarity. At the task level, we design an automatic soft clustering module to cluster similar tasks and generate the same initialization for similar tasks. Extensive offline experiments demonstrate that the TSAML framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123127936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3