Differentiable Perturb-and-Parse operator
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README.md

Differentiable Perturb-and-Parse operator

This repository contains the code for the continuous relaxation of the Eisner algorithm presented in:
"Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder",
Caio Corro, Ivan Titov

See: https://openreview.net/forum?id=BJlgNh0qKQ

To cite:

@InProceedings{perturb-and-parse,  
  author = "Corro, Caio and Titov, Ivan",  
  title = "Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder",  
  booktitle = "Proceedings of Seventh International Conference on Learning Representations",  
  year = "2019"  
}

The full VAE code and model will be released after the official proceedings release.
If any question, please contact me at following mail address: c.f.corro@uva.nl

Usage

#include "diffdp/dynet/eisner.h"

auto arcs = dynet::algorithmic_differentiable_eisner(
        weights, // input : matrix of arc weights
        difwfdp::DiscreteMode::ForwardRegularized, // relaxation mode
        diffdp::DependencyGraphMode::Adjacency, diffdp::DependencyGraphMode::Adjacency, // input/output format
        true // set to false to remove root arcs
);

Arguments

The following arguments must be provided:

  1. the arc-factored weights of dependencies
  2. the relaxation mode: diffdp::DiscreteMode::BackwardRegularized output the discrete structure and us the relaxation only for chart_backward, diffdp::DiscreteMode::ForwardRegularized use the relaxation during chart_forward
  3. the input format: diffdp::DependencyGraphMode::Adjacency use a adjacency matrix as input format, i.e. the main diagonal represent self connections and is never used, diffdp::DependencyGraphMode::Compact use the main diagonal to represent the weights of root dependencies
  4. the output format
  5. set to false to remove root arcs from the output

Batching

This computational node can be used with mini-batches. However, it does not implement the auto-batch functionnality of Dynet, so mini-batches should be constructed manually.

If sentences are of different sizes, a pointer of type "std::vector*" can be given as the last argument. This compatible with static graph (i.e. each chart_forward call will check sentence sizes in the vector)

WARNING: the size of batch input must not include the root node.

TODO

  • The memory usage could be divided by 2
  • Clean duplicate code
  • Static batch size (this could drastically save memory usage)