Efficient Purely Convolutional Text Encoding

In this section, we briefly list out all the features used in WMT 2013 (Luong et al., 2013) that were kept for this year, followed by some proposed features taking advantage of

To achieve such a goal, two categories of features are considered: high level features including, global, histogram and segmenta- tion features are extracted from word segments

Basic indexing techniques often construct one long string by con- catenating all the sequences together and use auxiliary data structures such as suffix trees and BWT [1], to build

To study the behavior of the inferential models simulated by word embedding, we wanted to identify some words that were used all along the entire Italian political spectrum.. Starting

Our contributions are the following: 1) We apply for the first time the Fully Convolutional Networks (FCN) classifier from TSC on activity recognition in smart homes. 2) We propose

Combined low level and high level features for Out-Of- Vocabulary Word detection.. Benjamin Lecouteux, Georges Linarès,

This paper addresses the issue of Out-Of-Vocabulary (OOV) words detection in Large Vocabulary Continuous Speech Recognition (LVCRS) systems.. We propose a method inspired by

The goal is to try to make all elements of an SIMD group operand (superword) scalable by the same amount, so that the scaling operations can be grouped together and implemented using