# Parallel Long Short-Term Memory for Multi-stream Classification

## TL;DR

This paper introduces Parallel LSTM (PLSTM), an innovative architecture for processing multiple synchronized input streams simultaneously, improving multi-stream sequence classification performance over existing models.

## Contribution

The paper proposes the first parallel LSTM architecture capable of handling multiple synchronized input sequences for improved classification accuracy.

## Key findings

- PLSTM outperforms baseline n-gram models.
- PLSTM surpasses existing LSTM approaches.
- Effective for multi-stream sequence classification.

## Abstract

Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells allow these DNN-based models to manage long-term dependencies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions. But most of the information available nowadays is from multistreams or multimedia documents, and require RNNs to process these information synchronously during the training. This paper presents an original LSTM-based architecture, named Parallel LSTM (PLSTM), that carries out multiple parallel synchronized input sequences in order to predict a common output. The proposed PLSTM method could be used for parallel sequence classification purposes. The PLSTM approach is evaluated on an automatic telecast genre sequences classification task and compared with different state-of-the-art architectures. Results show that the proposed PLSTM method outperforms the baseline n-gram models as well as the state-of-the-art LSTM approach.