Deconvolutional latent-variable model for text sequence matching

Published

Journal Article

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing learned latent codes with more semantic information and better generalization. Our model, trained in an unsupervised manner, yields stronger empirical predictive performance than a decoder based on Long Short-Term Memory (LSTM), with less parameters and considerably faster training. Further, we apply it to text sequence-matching problems. The proposed model significantly outperforms several strong sentence-encoding baselines, especially in the semi-supervised setting.

Duke Authors

Cited Authors

  • Shen, D; Zhang, Y; Henao, R; Su, Q; Carin, L

Published Date

  • January 1, 2018

Published In

  • 32nd Aaai Conference on Artificial Intelligence, Aaai 2018

Start / End Page

  • 5438 - 5445

Citation Source

  • Scopus