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]: Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met. A prominent limitation of current systems lies in the lack of matching and communication between the various technologies involved in the distant speech recognition process. The speech enhancement and speech recognition modules are, for instance, often trained independently. Moreover, the speech enhancement normally helps the speech recognizer, but the output of the latter is not commonly used, in turn, to improve the speech enhancement. To address both concerns, we propose a novel architecture based on a network of deep neural networks, where all the components are jointly trained and better cooperate with each other thanks to a full communication scheme between them. Experiments, conducted using different datasets, tasks and acoustic conditions, revealed that the proposed framework can overtake other competitive solutions, including recent joint training approaches.
[EX A]: A NETWORK OF DEEP NEURAL NETWORKS FOR DISTANT SPEECH RECOGNITION

[EX Q]: Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.
[EX A]: Exploring Automated Essay Scoring for Nonnative English Speakers

[EX Q]: We study the problem of estimating, in the sense of optimal transport metrics, a measure which is assumed supported on a manifold embedded in a Hilbert space. By establishing a precise connection between optimal transport metrics, optimal quantization, and learning theory, we derive new probabilistic bounds for the performance of a classic algorithm in unsupervised learning (k-means), when used to produce a probability measure derived from the data. In the course of the analysis, we arrive at new lower bounds, as well as probabilistic upper bounds on the convergence rate of the empirical law of large numbers, which, unlike existing bounds, are applicable to a wide class of measures.
[EX A]:
Learning Probability Measures with respect to Optimal Transport Metrics