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: In many domains it is desirable to assess the pref erences of users in a qualitative rather than quan titative way. Such representations of qualitative preference orderings form an important compo nent of automated decision tools. We propose a graphical representation of preferences that re flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep resentation is often compact and arguably natural. We describe several search algorithms for domi nance testing based on this representation; these algorithms are quite effective, especially in spe cific network topologies, such as chainand tree structured networks, as well as polytrees.

Output: Reasoning With Conditional Ceteris Paribus Preference Statements

Input: Consider Input: In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.

Output: HIGHWAY LONG SHORT-TERM MEMORY RNNS FOR DISTANT SPEECH RECOGNITION

Input: Consider Input: We study a semi-supervised learning method based on the similarity graph and Regularized Laplacian. We give convenient optimization formulation of the Regularized Laplacian method and establish its various properties. In particular, we show that the kernel of the method can be interpreted in terms of discrete and continuous time random walks and possesses several important properties of proximity measures. Both optimization and linear algebra methods can be used for efficient computation of the classification functions. We demonstrate on numerical examples that the Regularized Laplacian method is competitive with respect to the other state of the art semi-supervised learning methods. Key-words: Semi-supervised learning, Graph-based learning, Regularized Laplacian, Proximity measure, Wikipedia article classification Corresponding author. K. Avrachenkov is with Inria Sophia Antipolis, 2004 Route des Lucioles, 06902, Sophia Antipolis, France k.avrachenkov@inria.fr P. Chebotarev is with Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, 65 Profsoyuznaya Str., Moscow, 117997, Russia A. Mishenin is with St. Petersburg State University, Faculty of Applied Mathematics and Control Processes, Peterhof, 198504, Russia This work was partially supported by Campus France, Alcatel-Lucent Inria Joint Lab, EU Project Congas FP7-ICT-2011-8-317672, and RFBR grant No. 13-07-00990. L'Apprentissage Semi-supervise avec Laplacian Regularise Resume : Nous etudions une methode d'apprentissage semi-supervise, base sur le graphe de similarite et Laplacian regularise. Nous formalisons la methode comme un probleme d'optimisation convexe et quadratique et nous etablissons ses diverses proprietes. En particulier, nous montrons que le noyau de la methode peut etre interprete en termes des marches aleatoires en temps discret et continu et possede plusieurs proprietes importantes des mesures de proximite. Les techniques d'optimisation ainsi que les techniques d'algebre lineaire peuvent etre utilise pour un calcul efficace des fonctions de classification. Nous demontrons sur des exemples numeriques que la methode de Laplacian regularise est concurrentiel par rapport aux autres etat de l'art methodes d'apprentissage semi-supervise. Mots-cles : Apprentissage Semi-supervise, Apprentissage base sur le graphe de similarite, Laplacian regularise, mesure de proximite, classification des articles Wikipedia Semi-supervised Learning with Regularized Laplacian 3
Output: Semi-supervised Learning with Regularized Laplacian