TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Resume. Le crowdsourcing, un enjeu economique majeur, est le fait d'externaliser une tache interne d'une entreprise vers le grand-public, la foule. C'est ainsi une forme de sous-traitance digitale destinee a toute personne susceptible de pouvoir realiser la tache demandee generalement rapide et non automatisable. L'evaluation de la qualite du travail des participants est cependant un probleme majeur en crowdsourcing. En effet, les contributions doivent etre controlees pour assurer l'efficacite et la pertinence d'une campagne. Plusieurs methodes ont ete proposees pour evaluer le niveau d'expertise des participants. Ce travail a la particularite de proposer une methode de calcul de degres d'expertise en presence de donnees dont l'ordre de classement est connu. Les degres d'expertise sont ensuite consideres sur des donnees sans ordre pre-etabli. Cette methode fondee sur la theorie des fonctions de croyance tient compte des incertitudes des reponses et est evaluee sur des donnees reelles d'une campagne realisee en 2016.

SOLUTION: Une mesure d'expertise pour le crowdsourcing

PROBLEM: Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great diculty in practical applications. In this paper, we propose an ecient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more ecient variant TransEAdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

SOLUTION: Eicient Parallel Translating Embedding For Knowledge Graphs

PROBLEM: Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however that much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar↔️En) and compare it against a standard phrase-based translation system. We run extensive comparison using various configurations in preprocessing Arabic script and show that the phrase-based and neural translation systems perform comparably to each other and that proper preprocessing of Arabic script has a similar effect on both of the systems. We however observe that the neural machine translation significantly outperform the phrase-based system on an out-of-domain test set, making it attractive for real-world deployment.

SOLUTION:
First Result on Arabic Neural Machine Translation