Learning generic sentence representations using convolutional neural networks
© 2017 Association for Computational Linguistics. 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 encoder-decoder 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.
Gan, Z; Pu, Y; Henao, R; Li, C; He, X; Carin, L
Emnlp 2017 Conference on Empirical Methods in Natural Language Processing, Proceedings
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International Standard Book Number 13 (ISBN-13)