This paper investigates the impact of health cards in consumer health search (CHS) - people seeking health advice online. Health cards are a concise presentations of a health concept shown along side search results to specific health queries; they have the potential to convey health information in easily digestible form for the general public. However, little evidence exists on how effective health cards actually are for users when searching health advice online, and whether their effectiveness is limited to specific health search intents. To understand the impact of health cards on CHS, we conducted a laboratory study to observe users completing CHS tasks using two search interface variants: one just with result snippets and one containing both result snippets and health cards. Our study makes the following contributions: (1) it reveals how and when health cards are beneficial to users in completing consumer health search tasks, and (2) it identifies the features of health cards that helped users in completing their tasks. This is the first study that thoroughly investigates the effectiveness of health cards in supporting consumer health search.
{"title":"Health Cards for Consumer Health Search","authors":"Jimmy, G. Zuccon, B. Koopman, Gianluca Demartini","doi":"10.1145/3331184.3331194","DOIUrl":"https://doi.org/10.1145/3331184.3331194","url":null,"abstract":"This paper investigates the impact of health cards in consumer health search (CHS) - people seeking health advice online. Health cards are a concise presentations of a health concept shown along side search results to specific health queries; they have the potential to convey health information in easily digestible form for the general public. However, little evidence exists on how effective health cards actually are for users when searching health advice online, and whether their effectiveness is limited to specific health search intents. To understand the impact of health cards on CHS, we conducted a laboratory study to observe users completing CHS tasks using two search interface variants: one just with result snippets and one containing both result snippets and health cards. Our study makes the following contributions: (1) it reveals how and when health cards are beneficial to users in completing consumer health search tasks, and (2) it identifies the features of health cards that helped users in completing their tasks. This is the first study that thoroughly investigates the effectiveness of health cards in supporting consumer health search.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"160 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86445645","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}
{"title":"Session details: Session 7A: Relevance and Evaluation 1","authors":"M. Sanderson","doi":"10.1145/3349691","DOIUrl":"https://doi.org/10.1145/3349691","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81835731","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}
M. Albakour, Miguel Martinez, S. Tippmann, Ahmet Aker, J. Stray, Shiri Dori-Hacohen, Alberto Barrón-Cedeño
The journalism industry has undergone a revolution in the past decade, leading to new opportunities as well as challenges. News consumption, production and delivery have all been affected and transformed by technology Readers require new mechanisms to cope with the vast volume of information in order to be informed about news events. Reporters have begun to use natural language processing (NLP) and (IR) techniques for investigative work. Publishers and aggregators are seeking new business models, and new ways to reach and retain their audience. A shift in business models has led to a gradual shift in styles of journalism in attempts to increase page views; and, far more concerning, to real mis- and dis-information, alongside allegations of "fake news" threatening the journalistic freedom and integrity of legitimate news outlets. Social media platforms drive viewership, creating filter bubbles and an increasingly polarized readership. News documents have always been a part of research on information access and retrieval methods. Over the last few years, the IR community has increasingly recognized these challenges in journalism and opened a conversation about how we might begin to address them. Evidence of this recognition is the participation in the two previous editions of our NewsIR workshop, held in ECIR 2016 and 2018. One of the most important outcomes of those workshops is an increasing awareness in the community about the changing nature of journalism and the IR challenges it entails. To move yet another step forward, the goal of the third edition of our workshop will be to create a multidisciplinary venue that brings together news experts from both technology and journalism. This would take NewsIR from a European forum targeting mainly IR researchers, into a more inclusive and influential international forum. We hope that this new format will foster further understanding for both news professionals and IR researchers, as well as producing better outcomes for news consumers. We will address the possibilities and challenges that technology offers to the journalists, the challenges that new developments in journalism create for IR researchers, and the complexity of information access tasks for news readers.
{"title":"Third International Workshop on Recent Trends in News Information Retrieval (NewsIR'19)","authors":"M. Albakour, Miguel Martinez, S. Tippmann, Ahmet Aker, J. Stray, Shiri Dori-Hacohen, Alberto Barrón-Cedeño","doi":"10.1145/3331184.3331646","DOIUrl":"https://doi.org/10.1145/3331184.3331646","url":null,"abstract":"The journalism industry has undergone a revolution in the past decade, leading to new opportunities as well as challenges. News consumption, production and delivery have all been affected and transformed by technology Readers require new mechanisms to cope with the vast volume of information in order to be informed about news events. Reporters have begun to use natural language processing (NLP) and (IR) techniques for investigative work. Publishers and aggregators are seeking new business models, and new ways to reach and retain their audience. A shift in business models has led to a gradual shift in styles of journalism in attempts to increase page views; and, far more concerning, to real mis- and dis-information, alongside allegations of \"fake news\" threatening the journalistic freedom and integrity of legitimate news outlets. Social media platforms drive viewership, creating filter bubbles and an increasingly polarized readership. News documents have always been a part of research on information access and retrieval methods. Over the last few years, the IR community has increasingly recognized these challenges in journalism and opened a conversation about how we might begin to address them. Evidence of this recognition is the participation in the two previous editions of our NewsIR workshop, held in ECIR 2016 and 2018. One of the most important outcomes of those workshops is an increasing awareness in the community about the changing nature of journalism and the IR challenges it entails. To move yet another step forward, the goal of the third edition of our workshop will be to create a multidisciplinary venue that brings together news experts from both technology and journalism. This would take NewsIR from a European forum targeting mainly IR researchers, into a more inclusive and influential international forum. We hope that this new format will foster further understanding for both news professionals and IR researchers, as well as producing better outcomes for news consumers. We will address the possibilities and challenges that technology offers to the journalists, the challenges that new developments in journalism create for IR researchers, and the complexity of information access tasks for news readers.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89517465","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}
{"title":"Session details: Session 2C: Knowledge and Entities","authors":"A. D. Vries","doi":"10.1145/3349680","DOIUrl":"https://doi.org/10.1145/3349680","url":null,"abstract":"","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87591187","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}
Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort "by price" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.
{"title":"Fast Approximate Filtering of Search Results Sorted by Attribute","authors":"F. M. Nardini, Roberto Trani, Rossano Venturini","doi":"10.1145/3331184.3331227","DOIUrl":"https://doi.org/10.1145/3331184.3331227","url":null,"abstract":"Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort \"by price\" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)\"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88444377","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 fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.
{"title":"Learning from Fact-checkers: Analysis and Generation of Fact-checking Language","authors":"Nguyen Vo, Kyumin Lee","doi":"10.1145/3331184.3331248","DOIUrl":"https://doi.org/10.1145/3331184.3331248","url":null,"abstract":"In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named fact-checkers, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.","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":"88057025","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}
This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
{"title":"Web Table Extraction, Retrieval and Augmentation","authors":"Shuo Zhang, K. Balog","doi":"10.1145/3331184.3331385","DOIUrl":"https://doi.org/10.1145/3331184.3331385","url":null,"abstract":"This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88185123","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}
Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually estimate relevance based on document-level signals. Recent works consider a more fine-grain, passage-level relevance information, which can further enhance retrieval performance. However, it lacks a detailed analysis of how passage-level relevance signals determine or influence the relevance judgment of the whole document. To investigate the role of passage-level relevance in the document-level relevance judgment, we construct an ad-hoc retrieval dataset with both passage-level and document-level relevance labels. A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance. Based on the relationship between passage-level and document-level relevance, we also show that utilizing passage-level relevance signals can improve existing document ranking models. This study helps us better understand how users perceive relevance for a document and inspire the designing of novel ranking models leveraging fine-grain, passage-level relevance signals.
{"title":"Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment","authors":"Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma","doi":"10.1145/3331184.3331233","DOIUrl":"https://doi.org/10.1145/3331184.3331233","url":null,"abstract":"The understanding of the process of relevance judgment helps to inspire the design of retrieval models. Traditional retrieval models usually estimate relevance based on document-level signals. Recent works consider a more fine-grain, passage-level relevance information, which can further enhance retrieval performance. However, it lacks a detailed analysis of how passage-level relevance signals determine or influence the relevance judgment of the whole document. To investigate the role of passage-level relevance in the document-level relevance judgment, we construct an ad-hoc retrieval dataset with both passage-level and document-level relevance labels. A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance. Based on the relationship between passage-level and document-level relevance, we also show that utilizing passage-level relevance signals can improve existing document ranking models. This study helps us better understand how users perceive relevance for a document and inspire the designing of novel ranking models leveraging fine-grain, passage-level relevance signals.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86731504","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}
This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question "Which SERP is more relevant?'' and the other based on a diversity question "Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.
{"title":"Which Diversity Evaluation Measures Are \"Good\"?","authors":"T. Sakai, Zhaohao Zeng","doi":"10.1145/3331184.3331215","DOIUrl":"https://doi.org/10.1145/3331184.3331215","url":null,"abstract":"This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question \"Which SERP is more relevant?'' and the other based on a diversity question \"Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85783501","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}
Cross-modal retrieval takes one type of data as the query to retrieve relevant data of another type. Most of existing cross-modal retrieval approaches were proposed to learn a common subspace in a joint manner, where the data from all modalities have to be involved during the whole training process. For these approaches, the optimal parameters of different modality-specific transformations are dependent on each other and the whole model has to be retrained when handling samples from new modalities. In this paper, we present a novel cross-modal retrieval method, called Scalable Deep Multimodal Learning (SDML). It proposes to predefine a common subspace, in which the between-class variation is maximized while the within-class variation is minimized. Then, it trains m modality-specific networks for m modalities (one network for each modality) to transform the multimodal data into the predefined common subspace to achieve multimodal learning. Unlike many of the existing methods, our method can train different modality-specific networks independently and thus be scalable to the number of modalities. To the best of our knowledge, the proposed SDML could be one of the first works to independently project data of an unfixed number of modalities into a predefined common subspace. Comprehensive experimental results on four widely-used benchmark datasets demonstrate that the proposed method is effective and efficient in multimodal learning and outperforms the state-of-the-art methods in cross-modal retrieval.
{"title":"Scalable Deep Multimodal Learning for Cross-Modal Retrieval","authors":"Peng Hu, Liangli Zhen, Dezhong Peng, Pei Liu","doi":"10.1145/3331184.3331213","DOIUrl":"https://doi.org/10.1145/3331184.3331213","url":null,"abstract":"Cross-modal retrieval takes one type of data as the query to retrieve relevant data of another type. Most of existing cross-modal retrieval approaches were proposed to learn a common subspace in a joint manner, where the data from all modalities have to be involved during the whole training process. For these approaches, the optimal parameters of different modality-specific transformations are dependent on each other and the whole model has to be retrained when handling samples from new modalities. In this paper, we present a novel cross-modal retrieval method, called Scalable Deep Multimodal Learning (SDML). It proposes to predefine a common subspace, in which the between-class variation is maximized while the within-class variation is minimized. Then, it trains m modality-specific networks for m modalities (one network for each modality) to transform the multimodal data into the predefined common subspace to achieve multimodal learning. Unlike many of the existing methods, our method can train different modality-specific networks independently and thus be scalable to the number of modalities. To the best of our knowledge, the proposed SDML could be one of the first works to independently project data of an unfixed number of modalities into a predefined common subspace. Comprehensive experimental results on four widely-used benchmark datasets demonstrate that the proposed method is effective and efficient in multimodal learning and outperforms the state-of-the-art methods in cross-modal retrieval.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86340371","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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