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.

本论文介绍了一种名为平行 LSTM 的 RNN 模型，可以并行处理多个同步输入序列以预测共同输出。在自动电视节目分类任务上的实验表明，该方法优于基线 n-gram 模型和最先进的 LSTM 方法。