Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.Comment: Accepted to ACL 2022 (main conference

Research Roadmap of Service Ecosystems: A Crowd Intelligence Perspective

With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development

Application of Services Relation Tracing to Automated Web Service Composition

Web service composition has received much interest due to its potential to achieve the visionary promise of Service-Oriented Computing paradigm in which services can be utilized as the basic constructs to create flexible dynamic business processes and agile applications that may span organizations and computing platforms with little effort even automatically. In this paper, we proposed a snowball method for automated Web service composition which is based on service relation tracing which starts off rather like a standard ego-centered network study. It discoveries one or several available Web services which can accept the input parameters in user requirement through sematic reasoning and determines whether its output parameters meet the user requirements. And then it fetches other candidate services following the Sequential-total and Sequential-partial relations, investigates their outputs, also asks further sequential relations, and so forth through a succession of ”waves” of spreading. So the process is repeated and the composite service is constructed incrementally tracing sequential relations in Service Network just like a snowball. The experiment shows that the approach proposed in this paper can greatly improve the accuracy and efficiency of Web service composition

Automatic Clustering of Social Tag using Community Detection

Automatically clustering social tags into semantic communities would greatly boost the ability of Web services search engines to retrieve the most relevant ones at the same time improve the accuracy of tag-based service recommendation. In this paper, we first investigate the different collaborative intention between co-occurring tags in Seekda as well as their dynamical aspects. Inspired by the relationships between co-occurring tags, we designed the social tag network. By analyzing the networks constructed, we show that the social tag network have scale free properties. In order to identify densely connected semantic communities, we then introduce a novel graph-based clustering algorithm for weighted networks based on the concept of edge betweenness with high enough intensity. Finally, experimental results on real world datasets show that our algorithm can effectively discovers the semantic communities and the resulting tag communities correspond to meaningful topic domains

Introduction to the 3rd edition of the International Workshop on Adaptive Service-oriented and Cloud Applications ASOCA 2018

International audienceThe workshop address the adaptation and reconfiguration issues of the Service-oriented and cloud applications and architectures. ASOCA session gathered about twenty attendees. The discussions following the presentations. We received 16 submissions, out of which 2 papers were accepted. For this edition, the ASOCA program was merged with the program of the third workshop on IoT systems provisioning and management for context-aware smart cities (ISYCC'18). The presentations of the two workshops were held during the same session. We would like to thank the authors for their submissions, the program committee for their reviewing work, and the organizers of the ICSOC 2018 conference for their support which made this workshop possible. Workshop Organizer