rpryzant/proxy-a-distance

Proxy A-Distance

This is an implementation of an algorithm discussed in Ganin et. al (2015), Glorot et. al (2011), and Ben-David et. al (2007). It has been adapted for use with machine translation datasets, and released to the public under the MIT license.

This algorithm computes the Proxy A-Distance (PAD) between two domain distributions. PAD is a measure of similarity between datasets from different domains (e.g. newspapers and talk shows). The lower the PAD, the closer two datasets are.

The algorithm is as follows:

Mix the two datasets. Apply label that indicate each example's origin.
Train a classifier on these merged data.
Measure the classifier's error e on a held-out test set.
Set PAD = 2 (1 − 2e)

We use a linear bag-of-words SVM for the underlying classifier.

Requirements

numpy: pip install numpy
sklearn: pip install sklearn

Usage

python main.py [corpusfile 1] [corpusfile 2] [vocab file]

corpusfile 1 is a text file with one sentence per line.
corpusfile 2 is another text file with one sentence per line.
vocab is a text file with one token per line. These tokens represent a shared vocabulary for the above corpusfiles.

Example

python main.py test_data/europarl.en test_data/europarl.fr test_data/opensubtitles.en test_data/opensubtitles.fr test_data/vocab