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 language | English |
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Title of host publication | Seventh International Conference on Learning Representations (ICLR 2017) |
Number of pages | 10 |
Publication status | E-pub ahead of print - 9 May 2019 |
Event | Seventh International Conference on Learning Representations - New Orleans, United States Duration: 6 May 2019 → 9 May 2019 https://iclr.cc/ |
Conference
Conference | Seventh International Conference on Learning Representations |
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Abbreviated title | ICLR 2019 |
Country/Territory | United States |
City | New Orleans |
Period | 6/05/19 → 9/05/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- differentiable dynamic programming
- variational auto-encoder
- dependency parsing
- semi-supervised learning