Perturbed-Masking

This repo contains code to replicate experiments in the ACL'2020 Paper

Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT

Note that this not the original code we used when working on the paper. Since the huggingface transformers repo has been updated a lot (0.5.0 -> 2.5.1)

We cleaned and migrated the codes so that it works under the latest version of huggingface transformers and other packages that we used.

If you encountered with any problem, please report in "Issues".

Quick Start

After clone the repo, you can simply run the following command to try a dependency probe on PUD dataset.

Get impact matrix, take PUD dataset for example

python preprocess.py --cuda \
    --probe dependency \
    --data_split PUD \
    --dataset ./dependency/data/PUD.conllu

Now we do parsing, using Eisner algo

python parsing.py --probe dependency \
    --matrix ./results/dependency/bert-dist-PUD-12.pkl \
    --decoder eisner

Run the above command should give you an UAS of 41.7 (See Paper Tabel 1)

Download dataset

Since some of the dataset is not publicly available (E.g., PTB), we cannot include them in the repo. Please follow the instructions below to prepare the dataset:

Dependency

See papar for preprocess details.

Constituency

Down load the Penn Treebank corpus from LDC, and follow the dummy dirs/files in 'constituency/data' to organize the files.

Discourse

Download the SciDTB dataset from https://github.com/PKU-TANGENT/SciDTB, put the folder under 'discourse/'

Dependency Probe

See Quick Start

Constituency Probe

Get impact matrix, take WSJ23 dataset for example

python preprocess.py --cuda \
    --probe constituency \
    --data_split WSJ23 \
    --dataset constituency/data/WSJ/

Now we do parsing, using MART algo

python parsing.py --probe constituency \
    --matrix [the generated impact matrix file] \
    --decoder mart

Discourse Probe

Get impact matrix, take SciDTB test set for example

python preprocess.py --cuda \
    --probe discourse \
    --data_split SciDTB \
    --dataset ./discourse/SciDTB/test/gold/

Now we do parsing, using Eisner algo

python parsing.py --probe discourse \
    --matrix [the generated impact matrix file] \
    --decoder eisner

BERT trees in downstream task (ABSC)

To reproduce results in Tabel 5.

Git clone the PWCN repo https://github.com/GeneZC/PWCN
Follow their instructions to setup
Replace files in 'PWCN/datasets/semeval14/' with those of same name from 'Perturbed-Masking/ABSC/'. We provide the distance file we generated for quite reproduction.
Follow their instruction to train the model.

In the training, PWCN use random initialization and report the averaged performance, thus when re-run our experiments on a different machine, we observe slightly different results. We report all results below:

-	Model	Laptop-Acc	Laptop-F1	Restaurant-Acc	Restaurant-F1
PWCN paper	PWCN+Dep	76.12	72.12	80.96	72.21
Machine 1	PWCN+Dep	76.08	72.02	80.98	72.28
Machine 1	PWCN+Eisner	75.99	72.01	81.21	73.00
Machine 2	PWCN+Dep	75.17	70.66	80.67	71.92
Machine 2	PWCN+Eisner	74.82	70.90	80.66	72.16

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