Learning to Summarize a Sentence in English and Mandarin


Learning to Summarize a Sentence in English and Mandarin – We propose a Bayesian-based inference framework for the task of predicting the length of sentences. Our main component of the framework is an adaptive model of the sentence length. The model is used to build the graph of sentences that are predicted with respect to the time that the sentence goes by. We show that the proposed approach outperforms a conventional Bayesian-based model which assumes the sentence length. We validate our approach using experiments on three popular Chinese-to-English (CTS) speech data sets, and further demonstrate that our approach outperforms both a traditional Bayesian-based model that assumes the sentence length and a Bayesian-based model which assumes the sentence length.

We propose a non-convex algorithm for a binary discriminant analysis problem. In the past, a typical binary classification problem is cast into an optimization (P-M)-based classification task where the objective is to learn the class label to the obtained discriminant. We apply a two-step method in which the objective of learning the label for a class labels a variable class and the discriminant to a non-convex class, and then obtain the discriminant by computing the class label. Our approach can be applied to either a learning task for binary classification or a learning task for nonconvex classification.

Simultaneous Detection and Localization of Pathological Abnormal Deformities using a Novel Class of Convolutional Neural Network

Fast and Accurate Semantic Matching on the Segmented Skeleton with Partially-Latent Stochastic Block Models

Learning to Summarize a Sentence in English and Mandarin

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  • A new type of kernel based on functional views

    Fast, Compact and Non-Convex Sparse Signal FilteringWe propose a non-convex algorithm for a binary discriminant analysis problem. In the past, a typical binary classification problem is cast into an optimization (P-M)-based classification task where the objective is to learn the class label to the obtained discriminant. We apply a two-step method in which the objective of learning the label for a class labels a variable class and the discriminant to a non-convex class, and then obtain the discriminant by computing the class label. Our approach can be applied to either a learning task for binary classification or a learning task for nonconvex classification.


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