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.
|Original language||English (US)|
|Title of host publication||EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings|
|Publisher||Association for Computational Linguistics (ACL)firstname.lastname@example.org|
|Number of pages||11|
|State||Published - Jan 1 2017|