Improving multi-stream classification by mapping sequence-embedding in a high dimensional space

Abstract

Most of the Natural and Spoken Language Processing tasks now employ Neural Networks (NN), allowing them to reach impressive performances. Embedding features allow the NLP systems to represent input vectors in a latent space and to improve the observed performances. In this context, Recurrent Neural Network (RNN) based architectures such as Long Short-Term Memory (LSTM) are well known for their capacity to encode sequential data into a non-sequential hidden vector representation, called sequence embedding. In this paper, we propose an LSTM-based multi-stream sequence embedding in order to encode parallel sequences by a single non-sequential latent representation vector. We then propose to map this embedding representation in a high-dimensional space using a Support Vector Machine (SVM) in order to classify the multi-stream sequences by finding out an optimal hyperplane. Multi-stream sequence embedding allowed the SVM classifier to more efficiently profit from information carried by both parallel streams and longer sequences. The system achieved the best performance, in a multi-stream sequence classification task, with a gain of 9 points in error rate compared to an SVM trained on the original input sequences.