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.

Example Input: In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference. Our approach is aligned to mimic how a human does the natural language inference process given two statements. The model uses variants of Long Short Term Memory (LSTM), attention mechanism and composable neural networks, to carry out the task. Each part of our model can be mapped to a clear functionality humans do for carrying out the overall task of natural language inference. The model is end-to-end differentiable enabling training by stochastic gradient descent. On Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves better accuracy numbers than all published models in literature.
Example Output: A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference

Example Input: Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.
Example Output: SINGLE CHANNEL SPEECH ENHANCEMENT USING OUTLIER DETECTION

Example Input: Knowledge representation is a key component to the success of all rule based systems including learning classifier systems (LCSs). This component brings insight into how to partition the problem space what in turn seeks prominent role in generalization capacity of the system as a whole. Recently, knowledge representation component has received great deal of attention within data mining communities due to its impacts on rule based systems in terms of efficiency and efficacy. The current work is an attempt to find a comprehensive and yet elaborate view into the existing knowledge representation techniques in LCS domain in general and XCS in specific. To achieve the objectives, knowledge representation techniques are grouped into different categories based on the classification approach in which they are incorporated. In each category, the underlying rule representation schema and the format of classifier condition to support the corresponding representation are presented. Furthermore, a precise explanation on the way that each technique partitions the problem space along with the extensive experimental results is provided. To have an elaborated view on the functionality of each technique, a comparative analysis of existing techniques on some conventional problems is provided. We expect F. Shoeleh Computer Science and Engineering Dept. Shiraz University, Shiraz, Iran. E-mail: shoeleh@cse.shirazu.ac.ir M. Majd E-mail: majd@cse.shirazu.ac.ir A. Hamzeh E-mail: ali@cse.shirazu.ac.ir S. Hashemi E-mail: s hashemi@shirazu.ac.ir this survey to be of interest to the LCS researchers and practitioners since it provides a guideline for choosing a proper knowledge representation technique for a given problem and also opens up new streams of research on this topic.
Example Output:
Knowledge Representation in Learning Classifier Systems: A Survey