Wasserstein For Documents

Implementation for our ECIR 2018 paper "Fast Cross-lingual Document Retrieval using Regularized Wasserstein Distance". The code in the repository implements the Wass and Entro_Wass models of our paper. The implementations heavily rely on:

• Python Optimal Transport (POT) for calculating the wasserstein distances
• Scikit-learn for extending the nearest neighbors classifier to provide a generic framework for using out method.

Running the code

To run the code, one first needs to download the Numberbatch embeddings we used in this paper. We provide a script to download the embeddings and extract those for a subset of languages, e.g., English and French. To do that, first clone the repository, move the the directory and execute the script:

git clone https://github.com/balikasg/WassersteinRetrieval
cd WassersteinRetrieval
bash get_embeddings.sh

This will take some time as it downloads the embeddings (1.1GB compressed), uncompresses it, filters English and French embeddings and removes the files that are not needed. It will output informative messages for its progress. It will create two files: concept_net_1706.300.en and concept_net_1706.300.fr, the containing the English and French word embeddings respectively. The scripts can be improved to read from the compressed file directly using pythons gzip module if need be.

To run the cross-lingual retrieval experiments, run:

python emd.py concept_net_1706.300.en concept_net_1706.300.fr wiki_data/wikicomp.enfr.2k.en wiki_data/wikicomp.enfr.2k.fr 500 french

This runs the emd.py program with several arguments. The first two stand for the embeddings, the second two for the datasets where retrieval is performed, the fifth for the upper limit of words to be kept for each document (we used 500 for efficiency) and the last one for the second language (by default the first is english).

Citing

In case you use the model or the provided code, please cite our paper:

@InProceedings{balikas2018ecir,
  author    = {Georgios Balikas and Charlotte Laclau and Ievgen Redko and Massih-Reza Amini},
  title     = {Cross-lingual Document Retrieval using Regularized Wasserstein Distance},
  booktitle = {Proceedings of the 40th European Conference {ECIR} conference on Information Retrieval, {ECIR} 2018, Grenoble, France, March 26-29, 2018},
  year      = {2018}}

Timings

The code can be parallelized easily as one needs to calculate the distance of each query document with every document in the set of available documents. We have used pathos to parellize the calculations in the level of queries. Having N queries will send them to the available cores. In the figure below we illustrate the performance benefits when parallelizing the example of the section "Running the code", using 1,2,6,10,14,18 and 22 cores in a Intel(R) Xeon(R) CPU E5-2643 v3 @ 3.40GHz machine. Notice that Entro_Wass needs more time, but the difference is small when having more than 10 cores available. Also,Entro_Wass can be implemented with GPUs, but we did not have access to one while writing the paper.