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.

[EX Q]: 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.
[EX A]: Une mesure d'expertise pour le crowdsourcing

[EX Q]: Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optimal approach to optimizing such prediction problems based on noregret learning. Our method leverages a surprising result from online submodular optimization: a single no-regret online learner can compete with an optimal sequence of predictions. Compared to previous work, which either learn a sequence of classifiers or rely on stronger assumptions such as realizability, we ensure both data-efficiency as well as performance guarantees in the fully agnostic setting. Experiments validate the efficiency and applicability of the approach on a wide range of problems including manipulator trajectory optimization, news recommendation and document summarization.
[EX A]: Learning Policies for Contextual Submodular Prediction

[EX Q]: Acoustic word embeddings fixed-dimensional vector representations of variable-length spoken word segments have begun to be considered for tasks such as speech recognition and query-by-example search. Such embeddings can be learned discriminatively so that they are similar for speech segments corresponding to the same word, while being dissimilar for segments corresponding to different words. Recent work has found that acoustic word embeddings can outperform dynamic time warping on query-by-example search and related word discrimination tasks. However, the space of embedding models and training approaches is still relatively unexplored. In this paper we present new discriminative embedding models based on recurrent neural networks (RNNs). We consider training losses that have been successful in prior work, in particular a cross entropy loss for word classification and a contrastive loss that explicitly aims to separate same-word and different-word pairs in a "Siamese network" training setting. We find that both classifier-based and Siamese RNN embeddings improve over previously reported results on a word discrimination task, with Siamese RNNs outperforming classification models. In addition, we present analyses of the learned embeddings and the effects of variables such as dimensionality and network structure.
[EX A]:
DISCRIMINATIVE ACOUSTIC WORD EMBEDDINGS: RECURRENT NEURAL NETWORK-BASED APPROACHES