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DeepCT Efficiency Study

Introduction

This repo shows how to reproduce the experiments from the following SIGIR short paper: Efficiency Implications of Term Weighting for Passage Retrieval by Joel Mackenzie, Zhuyun Dai, Luke Gallagher, and Jamie Callan.

@inproceedings{mdgc20-sigir,
 author = {J. Mackenzie and Z. Dai and L. Gallagher and J. Callan},
 title = {Efficiency Implications of Term Weighting for Passage Retrieval},
 booktitle = {Proc. SIGIR},
 year = {2020},
 pages = {1821--1824}
}

Tools

We will use the PISA and Anserini search systems to build indexes of the MS-Marco DeepCT indexes. Please note that the original DeepCT codebase can be found here. We also use the CIFF tool for index exchange between Anserini and PISA.

These tools are included automatically as git submodules. You may need to run git submodule update --init --recursive to grab all of the tools and their dependencies.

Next, we'll build these tools. Refer to the README files in the respective repositories for detailed instructions.

echo "Building PISA"
cd pisa
mkdir build
cd build
cmake ..
make
cd ../../

echo "Building PISA CIFF"
cd pisa-ciff
cargo build --release
cd ../

echo "Building Anserini"
cd anserini
mvn clean package appassembler:assemble
cd eval
tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make
cd ../../../

echo "Building Anserini-CIFF"
cd anserini-ciff
mvn clean package appassembler:assemble
cd ../

End to End Runs

If you are interested in generating an end-to-end run, simply run the following scripts in order.

./prepare-data.sh
./build-indexes.sh
./search-effectiveness.sh
./search-efficiency.sh

Collections

We have four collections in jsonl format. They are the original MS-Marco corpus tokenized by BERT, and the DeepCT version (same tokenization process). For each index, both an 'unpruned' and a 'pruned' version are generated - the pruned versions remove all postings which DeepCT weighted to zero.

You can download and prepare these collections with the prepare-data.sh script. For step-by-step instructions, check out the data preparation guide.

Building the Indexes

Assuming you have built the tools above, you can run the build-indexes.sh tool which will automatically build all of the required data. If you would prefer to do it yourself, refer to the step-by-step indexing guide.

Running Effectiveness Experiments

The search-effectiveness.sh script will execute all runs and output their effectiveness values. The runs can be located in evaluate/run-files and a summary of the effectiveness scores can be gathered as follows:

cd evaluate
./evaluate-runs.sh

Running Efficiency Experiments

The search-efficiency.sh script will run the timing experiments and output per-query efficiency values. After the search-efficiency.sh script has been executed, the results can be found in timings/all.tsv as a tab-seperated file.

To get a summary of these timings:

cd timings
python3 tools/get-summary.py all.tsv