End-to-End Speech Translation of Arabic to English Broadcast News

Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language. ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence deep neural network models. This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system. Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level. These data was used to train and compare pipeline and end-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.Comment: Arabic Natural Language Processing Workshop 202

Applications des graphes en traitement d'images

International audienceLes graphes sont des outils de représentation des données très puissants et universels, utiles dans divers domaines des sciences notamment le traitement d'images, la reconnaissance des formes ou la vision par ordinateur. Dans ces domaines, la mesure de similarité entre les objets est souvent une phase importante. Dans le cadre d'une représentation sous forme de graphe cette phase se traduit par un appariement des graphes. Dans cet article nous passons en revue un ensemble de travaux basé sur la théorie des graphes appliquée aux traitements d'images. Notamment les approches de représentation en graphe et les différentes mesures de similarité de graphes. Également, nous présentons quelques applications des graphes dans l'analyse d'images et la recherche par le contenu

An Empirical Comparison of Graph Databases.

Abstract-In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, objectoriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, OrientDB, Titan and DEX

Indexation de graphes à partir d'une structure d'hypergraphe

International audienceDans ce papier, nous proposons une nouvelle méthode de clustering de graphes basée sur une modélisation d'hypergraphe. En premier lieu, nous appliquons un algorithme de sélection de prototype dédié aux bases de graphes où le nombre de prototype à sélectionner est déduit automatiquement. En second lieu, nous définissons une méthode de chevauchement des classes pour aboutir à la structure d'hypergraphe, où les hyperarcs sont les classes et les noeuds sont les graphes. Ainsi, un graphe peut être attribué à une ou plusieurs classes. L'originalité de notre approche réside dans la structure d'hypergraphe qui nous permet d'indexer une base de graphes à partir des centroïdes des hyperarcs. En plus, cette nouvelle approche permet de rechercher des graphes similaires à une requête et de naviguer dans une base de graphes en parcourant la structure d'hypergraphe

A hypergraph-based model for graph clustering: application to image indexing

Version finale disponible : www.springerlink.comInternational audienceIn this paper, we introduce a prototype-based clustering algorithm dealing with graphs. We propose a hypergraph-based model for graph data sets by allowing clusters overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we define a retrieval technique indexing the database with hyperedge centroids. This model is interesting to travel the data set and efficient to cluster and retrieve graphs

Attributed Graph Matching using Local Descriptions

Version final disponible : www.springerlink.comInternational audienceIn the pattern recognition context, objects can be represented as graphs with attributed nodes and edges involving their relations. Consequently, matching attributed graphs plays an important role in objects recognition. In this paper, a node signatures extraction is combined with an optimal assignment method for matching attributed graphs. In particular, we show how local descriptions are used to define a node-to-node cost in an assignment problem using the Hungarian method. Moreover, we propose a distance formula to compute the distance between attributed graphs. The experiments demonstrate that the newly presented algorithm is well-suited to pattern recognition applications. Compared with well-known methods, our algorithm gives good results for retrieving images

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

Median Graph Shift: A New Clustering Algorithm for Graph Domain

ISSN: 1051-4651 Print ISBN: 978-1-4244-7542-1International audiencen the context of unsupervised clustering, a new algorithm for the domain of graphs is introduced. In this paper, the key idea is to adapt the mean-shift clustering and its variants proposed for the domain of feature vectors to graph clustering. These algorithms have been applied successfully in image analysis and computer vision domains. The proposed algorithm works in an iterative manner by shifting each graph towards the median graph in a neighborhood. Both the set median graph and the generalized median graph are tested for the shifting procedure. In the experiment part, a set of cluster validation indices are used to evaluate our clustering algorithm and a comparison with the well-known Kmeans algorithm is provided

Evaluation of Graph Matching Measures for Documents Retrieval

International audienceIn this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used which include line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique

Comparing Graph Similarity for Graphical Recognition

The original publication is available at www.springerlink.com. 8th International Workshop, GREC 2009, La Rochelle, France, July 22-23, 2009. Selected PapersIn this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique