aditi138 / lase-agreement

A language structure explorer tool.

LASE: Automated Extraction of Agreement Rules

Requirements

A python version >=3.5 is required. Additional requirements are present in the requirements.txt

1. In the decision_tree_files.txt, enter the path to the treebanks for which you want to extract the rules. This code can work with UD/SUD dependency (v2.5) treebanks. To download all the SUD treebanks: https://surfacesyntacticud.github.io/data/

2. Run the following command to create the rules:

   python create_trees_website.py --folder_name website | tee output.log

Running this will create the html files with the decision trees, examples and other relevant information. Simply open the website/index.html in any browser to navigate the trees. This will also output the leaves as pickle files in the running folder. Example pickle files are provided in the outputs folder. In the output.log file the results for ARM metrics are stored in the order: setting, treebank, number of sentences, ARM This command by default uses the statistical thresholding.

3. To learn the rules with hard threshold, simply add the command --hard in the run command.

Low-resource experiments.

1. The five training runs for x=50,100,500 are present in the ./sud-data directory.
2. Run the following command to create the rules:

    python create_trees_website.py --folder_name website --simulate | tee transfer.log

This will output the ARM metric in transfer.log. To compute the average of all five runs, run the following:

    python  computeAverage.py --input transfer.log

Baseline

1. To run the baseline, we re-use the pickle files generated from before (which were stored in outputs/):

    python baseline.py --input outputs/ | tee baseline.out

This will output the results in baseline.out.

Creating the annotation site

1. Run:

    python create_triples.py
   

This will create the files for annotation under annotation_site/templates. Use the treebanks from decision_tree_files.txt.

2.To host it on the a flask server, run:

   python serve.py

This will host it on http://localhost:5000/el/. The triples selected for annotation are also outputted in the run folder. These are stored in annotation_site/templates as shown here. After annotation, the pickle files will be stored with the annotations.

We release the annotation results under annotation_site/.

Citing

If you make use of this software for research purposes, we will appreciate citing the following:

@inproceedings{chaudhary20emnlp,
    title = {Automatic Extraction of Rules Governing Morphological Agreement},
    author = {Aditi Chaudhary and Antonios Anastasopoulos and Adithya Pratapa and David R. Mortensen and Zaid Sheikh and Yulia Tsvetkov and Graham Neubig},
    booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    address = {Online},
    month = {November},
    year = {2020}}