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Ensemble EDU segmenter, sentencer and connective detector


Pre-trained models assume Python 3.6 and use the following libraries:

  • nltk
  • scikit-learn
  • pandas
  • numpy
  • scipy
  • xgboost
  • tensorflow-gpu
  • keras
  • pytorch
  • opencc-python-reimplemented
  • hyperopt

Note also that GPU support is required for the neural network libraries (i.e. CUDA+cuDNN 9 or 10). Additionally some external tools are used for parsing and tagging and not included, though pre-trained models are available. Please make sure to put:

External resources

After installing the required libraries, do the following:

  • Get word FastText word embeddings and put them in gumdrop/vec/; files are named like (note that paper results are based on trimmed embeddings containing the top 50,000 items for each language)
  • Get UDPipe models and put them in gumdrop/lib/udpipe/; files are named like spanish-gsd-ud-2.3-181115.udpipe (must begin with the language 'long name' and end in .udpipe)
  • Get TreeTagger models and put them in gumdrop/lib/treetagger/; files are named like spa.par
  • Put the shared task folder in data/, a sister folder of the gumdrop/ repo folder
  • If you want to auto-parse raw .tok data, create another folder data_parsed/ and run python -c all in the main system folder to reproduce auto sentence splitting and parsing with UDPipe.

Reproducing paper results

Results from the DISRPT2019 paper can be reproduced by running the following in Python 3.6, assuming the gumdrop/ folder is a sister of the shared task data/ folder.

Discourse unit segmentation

Run the following command (note this requires GPU support for the neural network):

python -c all --mode test --eval_test -o

This will print P/R/F scores to segmenter_results.log, as well as producing predicted output conll files for each test set (if the option -o is used). You can also get results for specific corpora using -c CORPUSNAME

To reproduce results on automatic parses, run:

python -c all --mode test --eval_test -o --auto

Note that this assumes that the automatically sentence splitted and UDPipe parsed files are already in a sister folder of gumdrop/, called data_parsed/. To reproduce the parse data from shared task input folders, see below.

Connective detection

This runs similarly to the discourse unit segmentation, for example:

py -3.6 -c zho.pdtb.cdtb --mode train-test --eval_test

Reproducing sentence splitting and automatic parses

To reproduce the included automatically parsed files, run:

python -c all

This assumes that data/ is a sister folder of gumdrop/ with complete shared task data, and that the parsed output should be placed in data_parsed/

Retraining the system

To retrain all models, run the respective modules as shown below. The option -b always loads stored best hyperparameters from the appropriate params/ subfolder. Retraining can take a long time, especially for RNNs. To train on auto-parse data, make sure the data is in data_parsed/ and add the --auto flag. If you are retraining the Ensemble, you will need to re-use the existing files in multitrain/, or else rerun multitraining using the --multitrain option for each trainable module. Note that a few rule based modules are not trainable (e.g.

python --mode train-test -b -c all
python --mode train-test -b -c all
python --mode train-test -b -c all

Re-optimizing hyperparameters

Hyperparameter optimization was carried using hyperopt on a GPU cluster, and can be expected to take long times. To re-optimize, add 'optimize-' to the mode switch, and set tune mode to -t hyperopt for some modules, as follows, for example:

python --mode optimize-train-test -t hyperopt -c spa.rst.sctb
python --mode tune -c spa.rst.sctb

Separate optimization should be carried out for automatic parses.


High accuracy ensemble EDU segmenter, sentencer and connective detector




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