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Pyserini: uniCOIL (w/ TILDE) for MS MARCO (V2) Passage Ranking

This page describes how to reproduce experiments using uniCOIL with TILDE document expansion on the MS MARCO V2 passage corpus, as described in the following paper:

Shengyao Zhuang and Guido Zuccon. Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion. arXiv:2108.08513.

The original uniCOIL model is described here:

Jimmy Lin and Xueguang Ma. A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques. arXiv:2106.14807.

In the original uniCOIL paper, doc2query-T5 is used to perform document expansion, which is slow and expensive. As an alternative, Zhuang and Zuccon proposed to use the TILDE model to expand the documents instead, resulting in a faster and cheaper process that is just as effective. For details of how to use TILDE to expand documents, please refer to the TIDLE repo. For additional details on the original uniCOIL design (with doc2query-T5 expansion), please refer to the COIL repo.

In this guide, we start with a version of the MS MARCO V2 passage corpus that has already been processed with uniCOIL + TILDE, i.e., gone through document expansion and term re-weighting. Thus, no neural inference is involved.

Data Prep

You can skip the data prep and indexing steps if you use our pre-built indexes. Skip directly down to the "Retrieval" section below.

We're going to use the repository's root directory as the working directory. First, we need to download and extract the MS MARCO V2 passage dataset with uniCOIL + TILDE processing:

# Alternate mirrors of the same data, pick one:
wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/msmarco-passage-v2-unicoil-tilde-expansion-b8.tar -P collections/
wget https://vault.cs.uwaterloo.ca/s/tb3m3J45HFJNAbq/download -O collections/msmarco-passage-v2-unicoil-tilde-expansion-b8.tar

tar -xvf collections/msmarco-passage-v2-unicoil-tilde-expansion-b8.tar -C collections/

To confirm, msmarco-passage-v2-unicoil-tilde-expansion-b8.tar is around 58 GB and should have an MD5 checksum of acc4c9bc3506c3a496bf3e009fa6e50b.

Indexing

We can now index these docs:

python -m pyserini.index.lucene \
  --collection JsonVectorCollection \
  --input collections/msmarco-passage-v2-unicoil-tilde-expansion-b8/ \
  --index indexes/lucene-index.msmarco-v2-passage-unicoil-tilde-expansion/ \
  --generator DefaultLuceneDocumentGenerator \
  --threads 12 \
  --impact --pretokenized

The important indexing options to note here are --impact --pretokenized: the first tells Pyserini not to encode BM25 doclengths into Lucene's norms (which is the default) and the second option says not to apply any additional tokenization on the uniCOIL tokens.

Upon completion, we should have an index with 138,364,198 documents. The indexing speed may vary; on a modern desktop with an SSD (using 12 threads, per above), indexing takes around 5 hours.

Retrieval

If you've skipped the data prep and indexing steps and wish to directly use our pre-built indexes, use --index msmarco-v2-passage-unicoil-tilde in the command below.

We can now run retrieval:

python -m pyserini.search.lucene \
  --index indexes/lucene-index.msmarco-v2-passage-unicoil-tilde-expansion \
  --topics msmarco-v2-passage-dev \
  --encoder ielab/unicoil-tilde200-msmarco-passage \
  --output runs/run.msmarco-v2-passage-dev-unicoil-tilde-expansion.txt \
  --batch 144 --threads 36 \
  --hits 1000 \
  --impact

Here, we are using the transformer model to encode the queries on the fly using the CPU. Note that the important option here is --impact, where we specify impact scoring. With these impact scores, query evaluation is already slower than bag-of-words BM25; on top of that we're adding neural inference on the CPU. A complete run should take around 30 minutes.

To evaluate, using trec_eval:

$ python -m pyserini.eval.trec_eval -c -M 100 -m map -m recip_rank msmarco-v2-passage-dev runs/run.msmarco-v2-passage-dev-unicoil-tilde-expansion.txt
Results:
map                   	all	0.1476
recip_rank            	all	0.1486

$ python -m pyserini.eval.trec_eval -c -m recall.100,1000 msmarco-v2-passage-dev runs/run.msmarco-v2-passage-dev-unicoil-tilde-expansion.txt
Results:
recall_100            	all	0.5630
recall_1000           	all	0.7733

There might be small differences in score due to platform differences in neural inference. The above score was obtained on Linux; macOS results may be slightly different.

Alternatively, we can use pre-tokenized queries with pre-computed weights. First, fetch the queries:

# Alternate mirrors of the same data, pick one:
wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/topics.msmarco-v2-passage.dev.unicoil-tilde-expansion.tsv.gz -P collections/
wget https://vault.cs.uwaterloo.ca/s/AAgRffaWQXdo8zi/download -O collections/topics.msmarco-v2-passage.dev.unicoil-tilde-expansion.tsv.gz

The MD5 checksum of the topics file should be 9c4fe0513cc8f45b44809f65c3c8bc20.

We can now run retrieval:

python -m pyserini.search.lucene \
  --index indexes/lucene-index.msmarco-v2-passage-unicoil-tilde-expansion \
  --topics collections/topics.msmarco-v2-passage.dev.unicoil-tilde-expansion.tsv.gz \
  --output runs/run.msmarco-v2-passage-dev-unicoil-tilde-expansion.txt \
  --batch 144 --threads 36 \
  --hits 1000 \
  --impact

Here, we also specify --impact for impact scoring. Since we're not applying neural inference over the queries, retrieval is faster, typically less than 10 minutes. To evaluate using trec_eval, follow the same instructions above. These results may be slightly different from the figures above, but they should be the same across platforms.

Reproduction Log*