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.

Input: Consider Input: Data can be acquired, shared, and processed by an increasingly larger number of entities, in particular people. The distributed nature of this phenomenon has contributed to the development of many crowdsourcing projects. This scenario is prevalent in most forms of expert/non-expert group opinion and rating tasks (including many forms of internet or on-line user behavior), where a key element is the aggregation of observations-opinions from multiple sources.

Output: Evaluating Crowdsourcing Participants in the Absence of Ground-Truth

Input: Consider Input: This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting. We explore supplementary use of word-based vectors trained on large amounts of unlabeled data. Our experiments for morphological tagging suggest that for "simple" model configurations, the choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or the augmentation with pre-trained word embeddings can be important and clearly impact the accuracy. Increasing the model capacity by adding depth, for example, and carefully optimizing the neural networks can lead to substantial improvements, and the differences in accuracy (but not training time) become much smaller or even negligible. Overall, our best morphological taggers for German and Czech outperform the best results reported in the literature by a large margin.

Output: Neural Morphological Tagging from Characters for Morphologically Rich Languages

Input: Consider Input: We show that the herding procedure of Welling (2009b) takes exactly the form of a standard convex optimization algorithm namely a conditional gradient algorithm minimizing a quadratic moment discrepancy. This link enables us to invoke convergence results from convex optimization and to consider faster alternatives for the task of approximating integrals in a reproducing kernel Hilbert space. We study the behavior of the different variants through numerical simulations. Our experiments shed more light on the learning bias of herding: they indicate that while we can improve over herding on the task of approximating integrals, the original herding algorithm approaches more often the maximum entropy distribution.
Output: On the Equivalence between Herding and Conditional Gradient Algorithms