Work

Learning Generic Sentence Representations Using Convolutional Neural Networks

Year: 2017

Type: conference-paper

Abstract: 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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d17-1254

Authors: Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He, Lawrence Carin

Institutions: Duke University, Microsoft (United States)

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid