We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoderdecoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.
REF explore a hierarchical model to predict multiple future sentences, using a convolutional neural network (CNN) encoder and a long-short term memory (LSTM) decoder.
Learning Generic Sentence Representations Using Convolutional Neural Networks
EMNLP 2017
arXiv: Computation and Language
Computer Science