Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder

Caio Corro, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings). To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing. As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters. Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores. We demonstrate effectiveness of our approach with experiments on English, French and Swedish.
Original languageEnglish
Title of host publicationSeventh International Conference on Learning Representations (ICLR 2017)
Number of pages10
Publication statusE-pub ahead of print - 9 May 2019
EventSeventh International Conference on Learning Representations - New Orleans, United States
Duration: 6 May 20199 May 2019
https://iclr.cc/

Conference

ConferenceSeventh International Conference on Learning Representations
Abbreviated titleICLR 2019
CountryUnited States
CityNew Orleans
Period6/05/199/05/19
Internet address

Keywords

  • differentiable dynamic programming
  • variational auto-encoder
  • dependency parsing
  • semi-supervised learning

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