We present a tool ("cwl_eval") which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the CWL framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The CWL framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost. Here we outline the CWL measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness.
{"title":"cwl_eval: An Evaluation Tool for Information Retrieval","authors":"L. Azzopardi, Paul Thomas, Alistair Moffat","doi":"10.1145/3331184.3331398","DOIUrl":"https://doi.org/10.1145/3331184.3331398","url":null,"abstract":"We present a tool (\"cwl_eval\") which unifies many metrics typically used to evaluate information retrieval systems using test collections. In the CWL framework metrics are specified via a single function which can be used to derive a number of related measurements: Expected Utility per item, Expected Total Utility, Expected Cost per item, Expected Total Cost, and Expected Depth. The CWL framework brings together several independent approaches for measuring the quality of a ranked list, and provides a coherent user model-based framework for developing measures based on utility (gain) and cost. Here we outline the CWL measurement framework; describe the cwl_eval architecture; and provide examples of how to use it. We provide implementations of a number of recent metrics, including Time Biased Gain, U-Measure, Bejewelled Measure, and the Information Foraging Based Measure, as well as previous metrics such as Precision, Average Precision, Discounted Cumulative Gain, Rank-Biased Precision, and INST. By providing state-of-the-art and traditional metrics within the same framework, we promote a standardised approach to evaluating search effectiveness.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"10 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88053218","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}
Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.
{"title":"One-Class Order Embedding for Dependency Relation Prediction","authors":"Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo","doi":"10.1145/3331184.3331249","DOIUrl":"https://doi.org/10.1145/3331184.3331249","url":null,"abstract":"Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86802197","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}
Factorization Machines (FM) have been widely applied in industrial applications for recommendations. Traditionally FM models are trained in batch mode, which entails training the model with large datasets every few hours or days. Such training procedure cannot capture the trends evolving in real time with large volume of streaming data. In this paper, we propose an online training scheme for FM with the alternating least squares (ALS) technique, which has comparable performance with existing batch training algorithms. We incorporate an online update mechanism to the model parameters at the cost of storing a small cache. The mechanism also stabilizes the training error more than a traditional online training technique like stochastic gradient descent (SGD) as data points come in, which is crucial for real-time applications. Experiments on large scale datasets validate the efficiency and robustness of our method.
{"title":"Training Streaming Factorization Machines with Alternating Least Squares","authors":"Xueyu Mao, Saayan Mitra, Sheng Li","doi":"10.1145/3331184.3331374","DOIUrl":"https://doi.org/10.1145/3331184.3331374","url":null,"abstract":"Factorization Machines (FM) have been widely applied in industrial applications for recommendations. Traditionally FM models are trained in batch mode, which entails training the model with large datasets every few hours or days. Such training procedure cannot capture the trends evolving in real time with large volume of streaming data. In this paper, we propose an online training scheme for FM with the alternating least squares (ALS) technique, which has comparable performance with existing batch training algorithms. We incorporate an online update mechanism to the model parameters at the cost of storing a small cache. The mechanism also stabilizes the training error more than a traditional online training technique like stochastic gradient descent (SGD) as data points come in, which is crucial for real-time applications. Experiments on large scale datasets validate the efficiency and robustness of our method.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78622288","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}
Guoxiu He, Yangyang Kang, Zhe Gao, Zhuoren Jiang, Changlong Sun, Xiaozhong Liu, Wei Lu, Qiong Zhang, Luo Si
It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.
{"title":"Finding Camouflaged Needle in a Haystack?: Pornographic Products Detection via Berrypicking Tree Model","authors":"Guoxiu He, Yangyang Kang, Zhe Gao, Zhuoren Jiang, Changlong Sun, Xiaozhong Liu, Wei Lu, Qiong Zhang, Luo Si","doi":"10.1145/3331184.3331197","DOIUrl":"https://doi.org/10.1145/3331184.3331197","url":null,"abstract":"It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79906582","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}
Steven Zimmerman, Alistair Thorpe, C. Fox, Udo Kruschwitz
Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.
{"title":"Investigating the Interplay Between Searchers' Privacy Concerns and Their Search Behavior","authors":"Steven Zimmerman, Alistair Thorpe, C. Fox, Udo Kruschwitz","doi":"10.1145/3331184.3331280","DOIUrl":"https://doi.org/10.1145/3331184.3331280","url":null,"abstract":"Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83852980","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}
In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.
{"title":"A Context-based Framework for Resource Citation Classification in Scientific Literatures","authors":"He Zhao, Zhunchen Luo, Chong Feng, Yuming Ye","doi":"10.1145/3331184.3331348","DOIUrl":"https://doi.org/10.1145/3331184.3331348","url":null,"abstract":"In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91422320","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}
Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not match the perceived similarity of human beings. Furthermore, the user's notion of similarity can drastically change with the exploration objective or task at hand. Therefore, this research proposes to investigate online adjustments to the similarity model using feedback generated during exploration or exploratory search. In this course, rich visualizations and interactions will support users to give valuable feedback. Based on this, metric learning methodologies will be applied to adjust a similarity model in order to improve the exploration experience. At the same time, trained models are considered as valuable outcomes whose benefits for similarity-based tasks such as query-by-example retrieval or classification will be tested.
{"title":"Document Distance Metric Learning in an Interactive Exploration Process","authors":"Marco Wrzalik","doi":"10.1145/3331184.3331420","DOIUrl":"https://doi.org/10.1145/3331184.3331420","url":null,"abstract":"Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not match the perceived similarity of human beings. Furthermore, the user's notion of similarity can drastically change with the exploration objective or task at hand. Therefore, this research proposes to investigate online adjustments to the similarity model using feedback generated during exploration or exploratory search. In this course, rich visualizations and interactions will support users to give valuable feedback. Based on this, metric learning methodologies will be applied to adjust a similarity model in order to improve the exploration experience. At the same time, trained models are considered as valuable outcomes whose benefits for similarity-based tasks such as query-by-example retrieval or classification will be tested.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87001484","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}
In many online services, anonymous commenting is not possible for the users; therefore, the users can not express their critical opinions without disregarding the consequences. As for now, naïve approaches are available for anonymous commenting which cause problems for analytical services on user comments. In this paper, we explore anonymous commenting approaches and their pros and cons. We also propose methods for anonymous commenting where it's possible to protect the user privacy while allowing sentimental analytics for service providers. Our experiments were conducted on a real dataset gathered from Instagram comments which indicate the effectiveness of our proposed methods in privacy protection and sentimental analytics. The proposed methods are independent of a particular website and can be utilized in various domains.
{"title":"On Anonymous Commenting: A Greedy Approach to Balance Utilization and Anonymity for Instagram Users","authors":"Arian Askari, Asal Jalilvand, Mahmood Neshati","doi":"10.1145/3331184.3331364","DOIUrl":"https://doi.org/10.1145/3331184.3331364","url":null,"abstract":"In many online services, anonymous commenting is not possible for the users; therefore, the users can not express their critical opinions without disregarding the consequences. As for now, naïve approaches are available for anonymous commenting which cause problems for analytical services on user comments. In this paper, we explore anonymous commenting approaches and their pros and cons. We also propose methods for anonymous commenting where it's possible to protect the user privacy while allowing sentimental analytics for service providers. Our experiments were conducted on a real dataset gathered from Instagram comments which indicate the effectiveness of our proposed methods in privacy protection and sentimental analytics. The proposed methods are independent of a particular website and can be utilized in various domains.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86014846","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}
Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma
Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.
{"title":"Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation","authors":"Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331205","DOIUrl":"https://doi.org/10.1145/3331184.3331205","url":null,"abstract":"Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88639624","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}
Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.
{"title":"Multi-Level Matching Networks for Text Matching","authors":"Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang","doi":"10.1145/3331184.3331276","DOIUrl":"https://doi.org/10.1145/3331184.3331276","url":null,"abstract":"Text matching aims to establish the matching relationship between two texts. It is an important operation in some information retrieval related tasks such as question duplicate detection, question answering, and dialog systems. Bidirectional long short term memory (BiLSTM) coupled with attention mechanism has achieved state-of-the-art performance in text matching. A major limitation of existing works is that only high level contextualized word representations are utilized to obtain word level matching results without considering other levels of word representations, thus resulting in incorrect matching decisions for cases where two words with different meanings are very close in high level contextualized word representation space. Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision. Experimental results on two widely used benchmarks, SNLI and Scaitail, show that the proposed MMN achieves the state-of-the-art performance.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86432677","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}
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