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]: Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On JapaneseEnglish we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.
[EX A]: Unfolding and Shrinking Neural Machine Translation Ensembles

[EX Q]: We consider a general framework of online learning with expert advice where the regret is defined with respect to a competitor class defined by a weighted automaton over sequences of experts. Our framework covers several problems previously studied, in particular that of competing against k-shifting experts. We give a series of algorithms for this problem, including an automata-based algorithm extending weightedmajority and more efficient algorithms based on the notion of failure transitions. We further present efficient algorithms based on a compact approximation of the competitor automaton, in particular efficient n-gram models obtained by minimizing the Rényi divergence, and present an extensive study of the approximation properties of such models. We also extend our algorithms and results to the framework of sleeping experts. Finally, we describe the extension of our approximation methods to online convex optimization and a general mirror descent setting.
[EX A]: Online Learning against Expert Automata

[EX Q]: We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e. g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports arbitrary numeric input features and text input and concatenates computed subbags to a final bag. It provides a variety of extensions and options. To our knowledge, openXBOW is the first publicly available toolkit for the generation of crossmodal bags-of-words. The capabilities of the tool are exemplified in two sample scenarios: time-continuous speech-based emotion recognition and sentiment analysis in tweets where improved results over other feature representation forms were observed.
[EX A]:
openXBOW – Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit