src

System

This folder contains code for reproducing our disentanglement experiments.

Requirements

The only dependency is the DyNet library, which can usually be installed with:

pip3 install dynet

Running

To see all options, run:

python3 disentangle.py --help

Train

To train, provide the --train argument followed by a series of filenames.

The example command below will train a model with the same parameters as used in the ACL paper. The model is a feedforward neural network with 2 layers, 512 dimensional hidden vectors, and softsign non-linearities.

python3 disentangle.py \
  example-train \
  --train ../data/train/*annotation.txt \
  --dev ../data/dev/*annotation.txt \
  --hidden 512 \
  --layers 2 \
  --nonlin softsign \
  --word-vectors ../data/glove-ubuntu.txt \
  --epochs 20 \
  --dynet-autobatch \
  --drop 0 \
  --learning-rate 0.018804 \
  --learning-decay-rate 0.103 \
  --seed 10 \
  --clip 3.740 \
  --weight-decay 1e-07 \
  --opt sgd \
  > example-train.out 2>example-train.err

Infer

This command will run the model trained above on the development set:

python3 disentangle.py \
  example-run.1 \
  --model example-train.dy.model \
  --test ../data/dev/*annotation* \
  --test-start 1000 \
  --test-end 2000 \
  --hidden 512 \
  --layers 2 \
  --nonlin softsign \
  --word-vectors ../data/glove-ubuntu.txt \
  > example-run.1.out 2>example-run.1.err

Note - the arguments defining the network (hidden, layers, nonlin), must match those given in training.

Evaluate

This command will run the output produced by the command above through the evaluation script:

python3 ../tools/evaluation/graph-eval.py --gold ../data/dev/*annotation* --auto example-run.1.out

The output should be something like:

g/a/m: 2607 2500 1855
p/r/f: 74.2 71.2 72.6

The first row is a count of the gold links, auto links, and matching links. The second line is the precision, recall, and F-score.

Note - the values in the paper are an average over 10 runs, so they will differ slightly from what you get here.

Running on a file

If you want to apply a model to a file, see this script for an example of how to do it: example-running.sh. The script is set up so someone could call it like so (once the necessary placeholders in the script are set):

./disentangle-file.sh < sample.ascii.txt > sample.links.txt

Ensemble

For the best results, we used a simple ensemble of multiple models. We trained 10 models as described above, but with different random seeds (1 through to 10). We combined their output using the majority_vote.py script in this directory.

The same script is used for all three ensemble methods, with slightly different input and arguments:

Union

./majority_vote.py example-run*graphs --method union > example-run.combined.union

Vote

./majority_vote.py example-run*graphs --method vote > example-run.combined.vote

Intersect

./majority_vote.py example-run*clusters --method intersect > example-run.combined.intersect

All of these assume the output files have been converted into our graph format. Assuming you run disentangle.py above and save the output of each run as example-run.1.out, example-run.2.out, example-run.3.out, etc, then this command will use one of our tools to convert them to the graph format:

for name in example-run*out ; do ../tools/format-conversion/output-from-py-to-graph.py < $name > $name.graphs ; done

The intersect method also assumes they have been made into clusters, like this:

for name in example-run*out ; do ../tools/format-conversion/graph-to-cluster.py < $name.graphs > $name.clusters ; done

Note: An earlier version of the steps above didn't account for a change in the output of the main system. Apologies for the broken output this would have caused.

C++ Model

As well as the main Python code, we also wrote a model in C++ that was used for DSTC 7 and the results in the 2018 arXiv version of the paper (the Python version was used for DSTC 8 and the 2019 ACL paper). The python model has additional input features and a different text representation method. The C++ model has support for a range of additional variations in both inference and modeling, which did not appear to improve performance. For details on how to build and run the C++ code, see this page.

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